Guidelines for Nutrition Surveys - Bangladesh

June 2015

Anthropometry, Mortality, IYCF, Food Security and WASH.

Funded By: ECHO

Prepared by:

IPHN, INFS, and UNICEF

ACKNOWLEDGEMENTS

This National Guidelines for Nutrition Surveys in Bangladesh are designed to provide clear instructions and guidance on area based nutrition surveys. I wish to convey my gratitude to the Director DGHS and the director IPHN/NNS for the strong leadership in preparing this guidelines. I do also acknowledge the contributions of the Program Manager and deputy program manager - Nutrition In Emergencies – under IPHN/NNS for their valuable administrative and technical support in the preparation of the guidelines.

I gratefully acknowledge the contribution of the members of the technical committee formed under IPHN to review the draft guidelines. Contributions by staff from the following organisations is who were members of the technical committee is highly appreciated.

- MOHFW - NNS/IPHN - UNICEF - Action Contre La Faim (ACF) - ICDDR,B - Institute of Nutrition and Food Science (INFS) of Dhaka University. I would also like to appreciate the time, review and useful recommendations provided by the curriculum committee towards finalisation of the guidelines. Thanks too to all the participants from government and development partners who attended and contributed at consultative workshops at national level.

Regards.

Name

ACRONYMS

CI Confidence Interval CDR Crude death rate DEFF Design effect DR Death Rate ENA Emergency nutrition assessment EPI Expanded programme on immunisation FCS Food Consumption Score FGD Focus group discussion GAM Global acute malnutrition HAZ Height-for-age z-score HFA Height-for-age IYCF Infant and young child feeding MAM Moderate acute malnutrition MUAC Mid upper arm circumference NCHS National centre for health statistics PPS Probability proportional to population size RNA Rapid nutrition assessment SAM Severe acute malnutrition SMART Standardized Monitoring and Assessment for Relief and Transitions WASH Water Sanitation and Hygiene WAZ Weight-for-age z-score WFA Weight-for-age WFH Weight-for-height WHM Weight-for-height median WHO World Health Organisation WHZ Weight for Height Z score

TABLE OF CONTENTS

ACKNOWLEDGEMENTS ...... 2 ACRONYMS ...... 3 TABLE OF CONTENTS ...... 4 1. INTRODUCTION ...... 9 2. BACKGROUND ...... 10 3. STEPS IN CONDUCTING NUTRITION SURVEYS ...... 11 3.1 Decide whether to conduct a survey ...... 12 3.2 Define survey objectives ...... 12 3.3 Define geographic area and population groups ...... 13 3.4 Meet local leadership and authorities ...... 13 3.5 Determine the timing of the survey ...... 14 3.6 Gather population data and other data ...... 14 3.7 Select the sampling method, determine sample size ...... 14 3.8 Decide which data to collect ...... 14 3.9 Prepare supplies and equipment ...... 15 3.10 Select and train survey teams ...... 15 3.11 Collect data and manage survey teams ...... 16 3.12 Enter and clean data...... 16 3.13 Analyze data and interpret results ...... 17 3.14 Write and disseminate the report ...... 17 4. SAMPLING ...... 18 4.1 Defining sampling ...... 18 4.2 Selecting the sampling method ...... 18 4.3 Setting parameters for sample size calculation...... 20 4.4 Simple/systematic random sampling ...... 24 4.5 Cluster sampling ...... 26 4.6 Adjustment for small sample size ...... 29 5. SURVEY FIELD PROCEDURES ...... 30 5.1 Bias ...... 30 5.2 Supervising data collection ...... 31 5

5.3 Special circumstances ...... 31 6. SELECTION AND TRAINING OF TEAMS ...... 33 6.1 Selection of survey teams ...... 33 6.2 Training of survey teams ...... 33 6.3 Roles and responsibilities of team members ...... 34 6.4 Standardisation test ...... 36 7. DATA COLLECTION ...... 39 7.1 Anthropometry ...... 39 7.2 Mortality ...... 45 7.3 Additional indicators ...... 47 8. DATA ENTRY AND CLEANING ...... 51 8.1 Data entry ...... 54 8.2 Plausibility check ...... 54 9. DATA ANALYSIS AND INTERPRETATION ...... 57 9.1 Data analysis ...... 57 9.2 Interpretation of results ...... 58 10. REPORT WRITING AND DISSEMINATION ...... 64 10.1 Preliminary Report ...... 64 10.2 Final report ...... 65

11. RAPID NUTRITION ASSESSMENTS ...... 67 11.1 Decide whether to conduct a RNA ...... 67 11.2 Define RNA objectives...... 68 11.3 Define geographic area and population groups ...... 68 11.4 Meet local leadership and authorities ...... 68 11.5 Determine the timing of the RNA ...... 68 11.6 Gather population data and other data ...... 69 11.7 Select sampling method, sample size ...... 69 11.8 Decide which data to collect ...... 71 11.9 Prepare supplies and equipment ...... 71 11.10 Select and train assessment teams ...... 71 6

11.11 Collect data and manage assessment teams ...... 71 11.12 Enter and clean data...... 73 11.13 Analyze data and interpret findings ...... 73 11.14 Write and disseminate the RNA report...... 75

Annex 1 WHO reference table ...... 76 Annex 2 Local calendar of events ...... 77 Annex 3 Referral form...... 78 Annex 4 Child health and nutrition module ...... 79 Annex 5 Mortality module ...... 81 Annex 6 IYCF module ...... 82 Annex 7 Food security module...... 84 Annex 8 Food security module...... 86 Annex 9 Survey questionnaires in Bengali ...... 88 Annex 10 Cluster control form ...... 93 Annex 11 MUAC screening form ...... 94 Annex 12 Key informant interview guidance sheet ...... 95 Annex 13 Focus group discussion guidance sheet ...... 96 Annex 14 Glossary of terms ...... 97 References ...... 99

LIST OF FIGURES Figure 1. Systematic random sampling ...... 19 Figure 2. Cluster sampling ...... 19 Figure 3. Effect of changing estimated prevalence on sample size ...... 20 Figure 4. Sample size calculation (SRS)...... 24 Figure 5. Random number generator ...... 25 Figure 6. Sample size calculation (cluster sampling) ...... 27 Figure 7. Modified EPI method ...... 28 Figure 8. Adjustment for small sample size ...... 29 Figure 9. Standardization test ...... 37 Figure 10. Indices of nutritional status ...... 39

Figure 11. Anthropometric equipment ...... 41 Figure 12. Salter hanging scale ...... 42 Figure 13. Mother-to-child scale ...... 42 Figure 14. Height measurement ...... 43 Figure 15. MUAC measurement ...... 44 Figure 16a. Identification of nutritional oedema ...... 44 Figure 16b. Nutritional oedema grade 2 ...... 44 Figure 16c. Nutritional oedema grade 3...... 44 Figure 17. Death rate calculation ...... 46 Figure 18. Death rate formula ...... 46 Figure 19. Conceptual framework of malnutrition...... 48 Figure 20. Variable view ...... 51 Figure 21. Data view ...... 52 Figure 22. Flagged values on data entry screen ...... 52 Figure 23. SMART and WHO flags ...... 53 Figure 24. Check for double entry ...... 54 Figure 25. Plausibility report ...... 54 Figure 26. Results anthropometry ...... 57 Figure 27. Results mortality ...... 58 Figure 28. Food Consumption Score weights ...... 61 Figure 29. Food Thresholds for FCS ...... 62 Figure 30. Timing of rapid assessments ...... 69 Figure 31. MUAC data analysis ...... 74

LIST OF TABLES Table 1. Precision: anthropometry ...... 21 Table 2. Precision: mortality ...... 21 Table 3. Cluster sampling example ...... 26 Table 4. Standardization test ...... 36 8

Table 5. Classification of acute malnutrition ...... 40 Table 6. Classification of nutritional oedema ...... 40 Table 7. Mortality rate calculation ...... 47 Table 8. Plausibility report criteria ...... 55 Table 9. Vitamin A supplementation and measles vaccination...... 59 Table 10. Morbidity results ...... 59 Table 11. Antenatal care and iron folate ...... 59 Table 12. IYCF analysis ...... 59 Table 13. Household analysis ...... 60 Table 14. Classification of severity of malnutrition ...... 62

1. INTRODUCTION.

According to the World Risk Report 2012, of 173 countries, Bangladesh is the 5th most disaster prone country in the world, particularly susceptible to devastating tropical cyclones, storm surges and floods. There are 27 million people across the 12 districts vulnerable to tropical cyclones and storm surges. Forty percent of these 12 districts, covering around 10 million people, are considered high risk areas and with an average poverty rate of 40%, people are particularly vulnerable to natural hazards. Malnutrition has remained high in Bangladesh, although a decline has been noted in stunting and underweight between 2004 and 2011 (Bangladesh 2011 DHS). Negative consequences of a cyclone on other sectors may aggravate the already poor nutritional status of the population. This in the background that under nutrition prevalence is chronically high in the coastal areas as is in the entire country. The nutritional status of especially under five children as well as pregnant and lactating women can deteriorate in quickly in the event of a disaster. It is therefore important to regularly assess the nutritional status in Bangladesh, particularly in disaster- prone areas. Currently, the Food Security Nutrition Surveillance Project (FSNSP), implemented by BRAC University in collaboration with Hellen Keller International, is the only source of seasonal, nationally representative estimates of malnutrition in Bangladesh. There is, however, scope to increase the coverage of nutrition surveys, and the development of these guidelines is one important step towards achieving the same.

These guidelines are designed to provide clear instructions and guidance to survey managers on nutrition surveys and rapid assessments by outlining steps and procedures to be followed in planning, implementation and evaluation of nutrition surveys and rapid assessments. The guidelines are divided into: a. Comprehensive nutrition survey guidelines, and b. Rapid nutrition assessment guidelines. The development of these guidelines has been triggered by the need for standardizing the approach and methodology used to conduct nutrition surveys and rapid assessments for the purposes of comparability of results and compliance to international standards. The methods and instructions are based on the SMART methodology (www.smartmethodology.org), which is based on the assessment of malnutrition and mortality to establish the magnitude of a crisis. The SMART Methodology draws from core elements of several existing methods and current best practices. Recommendations are based on varying degrees of evidence including methods for which there is clear scientific evidence to support its recommendation. A practical consideration that initiated the development of the SMART method, and guided the decision process in its development, is that partners should be able to collect data in nutrition surveys with a minimum of added burden to their programs. In addition, the method’s level of difficulty is a conscientious balance between technical soundness and simplicity for rapid assessment of acute emergencies to obtain early, accurate, quantitative profiles of a population’s nutritional status and mortality rate. For these reasons, the SMART method is iterative, with continuous upgrading and building on this basic version, informed by research, experience, and current best practices.

The current guidelines are to be used to undertake surveys in both emergency and none emergency contexts. The timing of nutrition surveys during an emergency should be as per the Joint Needs Assessment (JNA) Planning that propose detailed sectorial surveys at the 6-8 week after a disaster. However, rapid nutrition assessments could be done immediately - 1st week - 10

after a disaster either as a part of the JNA or as stand alone. (Please see page 69 for the diagram). In none emergency contexts, the guidelines should be used to undertake nutrition surveys to provide nutrition status information of the population for specific programmatic reasons.

2. BACKGROUND

In order to gain an understanding of the extent to which an emergency is impacting nutrition it is important to analyse data on the affected population and area. Data relating to nutrition can be collected, and existing evidence should be reviewed. Nutrition assessments are essential to guide response during an emergency. There are three main methods used to assess the nutrition of populations: rapid nutrition assessments, nutrition surveys and nutrition surveillance. In a chronic or complex emergency the situation is ongoing and nutrition surveillance is carried out. Anthropometric surveys are included as part of this; their purpose being to collect, analyse, interpret and report on information about the nutritional status of populations over time and to inform appropriate response strategies. However, in a rapid-onset emergency the priority is to obtain a snapshot of the nutrition situation as quickly as possible and therefore rapid nutrition assessments are carried out. The information may not always be representative and thus not statistically valid, but the results from a rapid assessment can verify the existence or threat of a nutrition emergency, provide an estimate of the numbers affected and establish immediate needs. Rapid assessments are also done where there is poor security and very limited access. Data in rapid assessments is collected directly from the field and is usually qualitative.

Acute malnutrition in children 6-59 months is closely linked with risk of death and is used to draw conclusions about the situation of the health status of the whole population, not just young children. Children aged 6-59 months are more vulnerable than other age groups to external factors (such as food shortage or illness) and their nutrition status is more sensitive to change than that of adults in many (although not all) populations. Mortality is the most critical indicator of a population’s improving or deteriorating health status and is the type of information to which donors and relief agencies most readily respond.

Nutrition surveys using a statistically representative sample of children remain the best method to determine the magnitude of malnutrition in a population. However, there are certain limitations to the use and interpretation of nutrition survey findings. Accurate population data is needed to list the population in villages or population units. This may not be available in an emergency. Additionally, the data cannot be disaggregated to produce statistically reliable results for geographical sub-samples when cluster sampling is used. Surveys are also time and resource consuming, but are often necessary to assess the anthropometric situation with accuracy. Interpreting results of anthropometric nutrition surveys in relation to contextual factors and interventions is also not straightforward and requires a wealth of information including food security and public health.

3. STEPS IN CONDUCTING NUTRITION SURVEYS.

This section describes the steps to be followed when conducting a nutrition and/or mortality survey. The steps for conducting nutrition surveys are as follows:

1. Decide whether to conduct a survey 2. Define survey objectives 3. Define geographic area and population groups 4. Meet local leadership and authorities 5. Determine the timing of the survey 6. Gather population data and other data 7. Select sampling method, determine sample size 8. Decide which data to collect 9. Prepare supplies and equipment 10. Select and train survey teams 11. Collect data and manage the survey team 12. Enter and clean data 13. Analyse data and interpret results 14. Write and disseminate the survey report

3.1 Decide whether to conduct a survey It is always important to make sure that the decision on whether or not to conducts a nutrition survey is made with consideration to the following points:  Avoiding overlap: the decision to undertake an assessment is usually made in conjunction with the government, the nutrition cluster and other agencies so as to prevent overlap by different agencies.  Are the results crucial for decision making? If a population’s needs are obvious, immediate program implementation takes priority over doing a survey, and the survey should be deferred. For example, after a natural disaster, such as a flood, where it is clear that the population’s food stocks have been destroyed, the current nutritional status may reflect the pre-disaster state.  It should be anticipated that the results will lead to action: there is little point of doing a survey if you know a response will not be possible. If the agency cannot itself implement a program where needed, the results must be useful in advocating for a response.  Is the affected population accessible? Insecurity or geographical constraints may result in limited access to the population of interest. If this is extreme, a survey cannot be conducted.

3.2 Define survey objectives It is important to be clear about what the survey seeks to achieve. In most cases, nutrition surveys seek to quantify the level of malnutrition (and/or mortality) in a given population at a defined point in time. Nutrition surveys also provide a baseline from which future trends can be monitored. Nutrition and mortality surveys also provide opportunity for collecting additional data on relevant interventions and nutrition-related variables such as food security. These include immunization and nutrition program coverage, vitamin A, anaemia, or other micronutrient deficiency and morbidity. However, caution must be exercised given that a survey presents a greater likelihood of inaccuracy as more data is included.

The broad objective (aim) of a nutrition survey is to assess the current nutrition and health status of a specific population. (The population may be the district, village, camp or urban settlement, or even the region or country).

The specific objectives may vary depending on the interventions, situations or circumstances in place or intended, and may include the following:

 To determine the prevalence of malnutrition (wasting, stunting and Under weight) among children aged 6‐59 months.  To determine the nutritional status of a specific sub‐group (e.g. women of reproductive age, adolescents or the elderly).  To determine the coverage of health interventions (e.g. measles vaccinations, Vitamin A supplementation and oral polio vaccine) among children aged 6‐59 months.  To determine the levels of retrospective crude mortality rates and age- specific mortality rates for under‐5s in a specific time period.  To determine the incidence of common diseases (diarrhoea, measles and ARI) among the target population, two weeks prior to the assessment.  To identify possible interventions that addresses the causal factors of malnutrition.

Note that, in defining specific objectives, the target group must be specified where applicable. The objectives must be measurable, and should be feasible within the context of a nutrition survey, bearing mind the limitations of nutrition surveys due to their cross-sectional nature, which does not allow for determining the cause-and-effect.

3.3 Define geographic area and population groups In planning a nutrition survey, a decision must be made as to the area and population groups that will be covered, with clear justification. It is useful to have a map of the selected area for reference, and for inclusion in the final survey report.

A survey should be conducted in an area where the population is expected to have a similar nutritional and mortality situation. If an area is assessed that has two or more very different agro- ecological zones, the results will be an average of the two zones and not give an appropriate perspective of either zone. Such heterogeneity can be resolved by doing separate assessments, although this usually increases the cost. In general, urban and rural areas, refugee/IDP, and resident populations should be assessed separately. If there are areas which are unreachable due to insecurity, these must be defined before the survey and must be reported as having been excluded from the survey. Anthropometric measurements and oedema assessments for children ages 6 to 59 months, and crude death rate (CDR) for the entire population (all deaths within a defined period of time) are the priority for nutrition and mortality surveys. The 6 to 59 month-old children are considered the most sensitive to acute nutritional stress and thus a proxy of the severity in the whole population. Globally, there is also more experience in collecting data from this age group.

3.4 Meet local leadership and authorities Meeting local leadership and authorities before a survey is important for the following reasons:

• To obtain letters of permission from the local authorities addressed to the district or village leaders, stating that you will be conducting the survey, stating the reasons for the survey. • It is necessary to agree with the community about the objectives of the survey. If the population does not understand why you are doing an assessment they may not 13

cooperate during the survey.

• To obtain a map of the area to plan the survey.

• To obtain detailed information on population figures.

• To obtain information on security and access to the prospective survey area.

• To agree on the dates of the survey with the community and local authorities.

• To agree on how the results will be disseminated and used and, in particular, to discuss the likely intervention if the situation is found to be as poor as expected.

3.5 Determine the timing of the survey The exact dates of the assessment should be chosen with the help of community leaders and local authorities to avoid market days, local celebrations, food distribution days, vaccination campaigns, or other times when people are likely to be away from home. Roads may be impassable during the rainy season. In agricultural areas, women may be in the fields for most of the day during ground preparation, planting, or harvesting. Wherever possible, community leaders should inform the villages chosen to be surveyed in advance. In determining the timing of a survey, it may be decided that a survey be conducted at the start of an intervention, and then at the end, so as to determine the impact of an intervention. This may be challenging, as there may be several factors contributing to the impact of an intervention and it may be difficult to establish attribution. Nutrition surveys may also monitor trends of malnutrition and identify possibly impact of a crisis, which is generally a more relevant rationale.

3.6 Gather population data and other data Before starting the survey, it is important to learn as much about the population as possible from existing sources, including population characteristics and figures. Population figures are key for sample size calculation. Data on previous surveys and assessments, health statistics, food security information, situation reports (security and political situation), maps, and anthropological, ethnic, and linguistic information is also important.

3.7 Select the sampling method, determining sample size A decision must be made as to which sampling method to apply, based on knowledge about the size of the population, the layout of households, and the presence of household lists. Simple random sampling, systematic random sampling and cluster sampling are the three methods recommended by SMART. With a small sample size, exhaustive sampling may be used. The population data is then used to calculate the required sample size for the survey, which assists in anticipating the expected duration and required personnel and equipment.

3.8 Decide which data to collect The objectives of the survey will guide the decision on what data will be collected, and hence what materials and equipment could be required.

Nutrition For determination of the magnitude and related factors influencing malnutrition, data normally collected includes: 14

1. age, in months (from a known date of birth or based on an estimate derived from a calendar of local events) 2. sex 3. weight in kilograms 4. height, in centimetres 5. presence or absence of oedema 6. mid-upper arm circumference (MUAC) 7. measles immunization 8. micronutrient supplementation status, particularly vitamin A 9. infant and young child feeding practices 10. morbidity information

Mortality To estimate the mortality rate (and causes of death), the following information needs to be collected:

1. total number (of all ages) currently in the household 2. number who were in the household at the start of the recall period 3. number of deaths 4. number of births 5. number who left the household during the recall period 6. number who joined the household during the recall period 7. age and sex of each household member 8. number of deaths of children below age 5 9. information about cause of death

Additional data to collect may include: -Food security -Water, sanitation and hygiene

3.9 Prepare supplies and equipment It is essential to take measures to ensure that measuring material, including scales and height boards, are procured in good time and are in good condition. During the survey, scales should always be calibrated each day against a known weight. Faulty equipment should never be used and there should always be spare equipment. Additional equipment and supplies include: vehicles, fuel, paper and pens, questionnaires, and referral forms.

3.10 Select and training survey teams Team members do not have to be health professionals. In fact, anyone from the community can be selected and trained. They need to be fit, as there is usually a lot of walking. They should have a relatively high level of education, as they will need to read and write fluently, and count accurately. Ideally, they will speak the local language. If not possible, there should be interpreters as part of the survey teams. Women generally have much more experience dealing with young children and should usually lead the interviewing of mothers/caretakers of children. This is also important as some cultures do not allow women to be interviewed by men. The gender composition of the team should conform to the local context.

The composition of survey teams depends on the data to be collected. Two people are required

for measurement of children (measurer and recorder) in addition to an interviewer. A team leader is also required for quality control and leadership of the survey team. If there are additional modules such as food security and water and sanitation, which are household modules, an additional member may be required.

Generally, four to six teams survey teams may be needed depending upon the number of households to be visited, the time allocated to complete the survey, and the size and the accessibility of the area covered. The number of teams should never be too many despite the fact that the more the teams, the faster the data collection. The quality of the data deteriorates with too many teams as it is much more difficult to train, supervise, provide transport and equipment, and organize a large number of teams. Supervisors should be assigned to each team. If the teams are to collect data in nearby areas, there may be a supervisor for two teams, but if they are far apart, a supervisor may be required for each team. The supervisor must be experienced in undertaking nutrition and mortality surveys, training team members, organizing logistics, and managing people.

Adequate training of the survey team members before the survey is crucial. All scheduled training must be completed prior to data collection, and every team member should undergo exactly the same training, whatever their former experience, to ensure standardization of methods. During the survey the supervisor must continually reinforce good practice, identify and correct errors, and prevent declining measurement standards.

3.11 Collect data and manage the survey team After having trained survey teams and assigning members to respective teams, the data collection is ready to begin. Supervisors have the overall responsibility of management of survey teams in the field. The supervisor must ensure that households are selected properly, ensuring the equipment is checked and calibrated each morning during the survey and that measurements are taken and recorded accurately. Unexpected problems nearly always arise during a survey, and the supervisor is responsible for deciding how to overcome them. Each problem encountered and decision made must be promptly recorded and included in the final report. The survey supervisor is also responsible for overseeing data entry and for the analysis and report writing.

The survey manager should organise a review session at the end of each day for a discussion on the day’s progress and any possible challenges. Before leaving the field, each team leader should review and sign all forms to ensure that no pieces of data have been left out. If there were people absent from the house during the day, the team should return to the household at least once before leaving the area.

It is also the duty of the supervisor to regularly supervise teams in the field. It is particularly important to check cases of oedema, as there are often no cases seen during the training and some team members may therefore be prone to mistaking a fat child for one with oedema (particularly with younger children). The supervisor should note teams that report a lot of oedema, and visit some of these children to verify their status.

The survey teams must be managed in such a way that they are not overworked, as this may introduce bias due to short cuts and errors. This is achieved by the survey manager making a realistic determination of the number of households which a team can realistically complete in a day without fatigue.

3.12 Enter and clean data It is important to note that data cleaning in nutrition surveys begins from the moment data 16

collection begins, rather than at the end. By conducting data cleaning as data collection proceeds, errors can be swiftly rectified to enhance the accuracy of data collected.

The process begins with the team leaders, who must check the questionnaires during the day for errors, which may include omissions. Each evening, or during the next day while the teams are in the field, the supervisor should arrange for data to be entered into the computer. Recording errors, unlikely results, and other problems with the data may become clear at this stage. The ENA for SMART software will automatically flag abnormal values as data are entered. Each morning, before the teams set out for the day, there should be a short feedback session. If any team is getting a large number of “flagged” results, the supervisor should accompany that team the next day. If the results are very different from those obtained by the other teams, it may be necessary to repeat the cluster from the day before.

3.13 Analyze data and interpret results ENA for SMART is recommended for analysis of anthropometry data, which may either be entered directly, or copied from other software such as Microsoft Excel. Individual-level data on additional indicators may be analyzed with other software such as EPI-INFO given the limitations for ENA for SMART. Similarly, household-level data may neither be entered in nor analyzed in ENA for SMART.

3.14 Write and disseminate the report The final part of a survey is report writing and dissemination. The results of the survey should be presented in a standard format so that different surveys can be compared, and no important information should be left out. After being introduced to the standard format, it is also becomes much easier for readers to find particular pieces of information in the report. ENA for SMART automatically generated a standard report format with standard headlines, which the survey manager can build upon. This is very convenient for the survey manager.

Preliminary results from a nutrition survey should be available within approximately a week, with the full report being available within a month, assuming that there are no unforeseen problems. The survey report must clearly articulate the objectives, implementation steps and findings of the survey in clear language. An important aspect of the report is recommendations for possible intervention.

4. SAMPLING This section will define and explain the different procedures and methods for sampling in nutrition surveys, followed by a step-by-step description of procedures for sample size calculation.

4.1 Defining sampling If all the children aged 6-59 months from a given population were measured, we would get a precise picture of the nutrition status of the population. This is called a census, or exhaustive survey, and it is possible in a small population. However, an exhaustive survey is normally long, costly and difficult to carry out in a large population. Instead of surveying all the children, we normally survey only a sub-group of the population, called a sample, which “represents” the whole population. Instead of interviewing all the households and measuring all the children, a sample may be taken to represent the whole population. It is important that the sample be chosen that indeed is representative of the whole population. This is done by choosing

households at random, whereby the selection of one household is independent of the selection of another, so as to give each household and child in the population an exactly equal chance of being selected into the sample.

4.2 Selecting the sampling method Box 1 summarizes the decision-making process for selecting the suitable sampling method for a nutrition survey.

Box 1: Sampling method decision-making Large population (eg. Relatively small population

District, region) (eg. Camp, small urban

No settlement)

Updated list of Updated list of households not available households available

Households arranged in clear pattern

Simple random Cluster sampling Systematic random sampling sampling

Simple random sampling When a complete and updated list of households is available and the population is relatively small, it is recommended to use simple random sampling, whereby each household is randomly selected using a random number generator.

Systematic random sampling Where the population is relatively small and the numbers of households are known, households may be arranged in a clear pattern as shown in Figure 1, with survey teams able to move systematically from one household to another. In this case, systematic random sampling is used. This method is a variant of simple random sampling. In this method, a sampling interval is determined by dividing the total number of households by the required number of households. A random number is then selected between 1 and the sampling interval to determine the starting point.

Figure 1 Systematic random sampling 18

From the starting point, the sampling interval is applied continuously to select subsequent households until the sample has been achieved. Simple or systematic random sampling is normally useful in contexts such as small refugee camps and urban settlements.

Cluster sampling In a relatively large population such as a district, cluster sampling is more preferable as it may not be feasible for teams to travel long distances if households are to be randomly selected and may be far apart. Another reason is that the likelihood of having an updated list of all households in a large area is very low. This method is implemented in two stages. There are two stages, which are: 1. selection of clusters and, 2. selection of households (Figure 2) Stage 1:The whole population is divided, on paper, into smaller Figure 2 Cluster sampling discrete geographical areas, such as villages as in the case of a district-level survey. The population of each smaller area must be known or page42 be estimated with reasonable accuracy. Clusters are then randomly selected from these villages with the chance of any village being selected being proportional to the size of its population. This is called sampling with “probability proportional to population size” (PPS). Stage 2: Households are chosen at random from within each cluster area or village.

4.3 Setting parameters for sample size calculation In order to calculate the required sample size for a nutrition survey, the following parameters are considered:

Estimated prevalence/death rate Anthropometry The estimated prevalence of acute malnutrition is estimated, and can be estimated from previous survey data, or surveillance data. Emergency thresholds may also be used when previous data is unavailable. It must be noted that, to determine the sample size it is recommended that the most conservative value be selected, which is the prevalence as close to 50% as possible, given that the required sample size increases as estimated prevalence increases up to 50% (Figure 3).

Figure 3 Effect of changing estimated prevalence on sample size 19

Source: SMART Methodology (2012)

Mortality The expected Crude Death Rate (CDR) in a mortality survey can also be estimated from previous surveys or from discussion with key informants. It may also, in the absence of these sources, be set as CDR of 2 deaths per 10,000 per day, which is the level that is often used to declare an emergency (WHO, 1995).

Standard error, probability and sampling interval Data gathered from a sample population only provides an estimate of the true population value. Thus the true population value can only be calculated through exhaustive sampling (by measuring every child in the population). Hence, whenever a sample is drawn, there is a risk that it will not be truly representative and, therefore, that the results do not reflect the true situation. Inevitably, if a second sample is drawn from the same population, slightly different results are likely to be obtained. This risk is known as the standard error. In anthropometric surveys, the generally accepted standard error is five per cent. That is to say that if a hundred sample surveys were carried out on the same population, five would give results that were not representative of the total population. When we undertake a survey, therefore, we calculate not only an estimate of the rate of malnutrition but also the range of values within which the real rate of malnutrition in the entire population almost certainly lies. This range is usually called the confidence interval (C.I). In nutrition surveys we generally accept that a 95 per cent confidence interval is appropriate (5 per cent standard error). This means that we are 95 per cent certain that the true prevalence of malnutrition lies in the range given. If a survey found the prevalence of global acute malnutrition (GAM) to be 29.7% (23.8-36.4 95% CI), this would mean that we are 95% confident that the true GAM prevalence lies between 23.8% and 36.4%. The 95% is automatically calculated when using ENA for SMART software.

Precision Precision measures the consistency of the results and is related to sampling error. The larger the proportion of the target population that is measured, the lower this uncertainty becomes. Therefore, the higher the sample size, the higher the precision. A larger sample size increases the precision of the results.

Table 1 Precision: anthropometry Malnutrition Confidence Desired prevalence Interval precision % Range ± % 5 3 – 7 2.0 7.5 5 – 10 2.5 10 7 – 13 3.0 13 10 – 16 3.0 15 11 – 19 3.0 20 15 – 25 5.0 30 22.5 – 37.5 7.5 20

40 30 – 50 10.0

There are recommended ranges for precision which are recommended by the SMART methodology for different levels of prevalence of malnutrition and death rate (Table 1 and 2): Table 2 Precision: mortality Confidence Desired CDR Interval precision ∕10,000∕day Range ± ∕10,000∕day 0.5 0.2 – 0.8 0.30 1.0 0.6 – 1.4 0.40 1.5 1.0 – 2.0 0.50 2.0 1.25 – 2.75 0.75 3.0 2.0 – 4.0 1.00

Design effect The design effect (DEFF) is a correction factor to account for the heterogeneity between clusters with regard to the measured indicator. Therefore, it is only used to determine sample size in cluster sampling. Generally, if there is no previous information about design effect, 1.5 can be used as a default for GAM. DEFF depends on the prevalence and the size of the clusters. The higher the expected prevalence, the higher would be DEFF. For example, if your expected prevalence is around 10%, expected DEFF may be 1.5, whereas if expected prevalence is around 25-30% you would increase your expected DEFF to 1.7-1.8. The smaller the number of children per cluster, the smaller the DEFF will be. For example, if you are measuring 15 children per cluster, your DEFF may be 1.5, whereas if you plan to measure 25-30 children per cluster, you would increase your expected DEFF to 1.7-1.8. If heterogeneity is expected to be high, the maximum value used is 2. DEFF multiplies the sample size, meaning that a DEFF of 2 doubles the required sample size. A higher DEFF than 2 would mean that more than one survey must be conducted due to very high heterogeneity. In this case, stratified sampling may be considered.

Recall period In mortality surveys, a recall period, in days, is applied, and is defined as the interval over which deaths are counted. It is determined by looking at the period most relevant to the purposes of the survey, the risk of mortality being measured, and the context of the study. To improve the accuracy of mortality estimates in cross-sectional surveys, the beginning of the recall period should be a memorable date known to everyone in the population. For example, the start of the recall period may be a major holiday or festival (Christmas, beginning of Ramadan, etc.), an election, an episode of catastrophic weather or other remarkable event. The end of the recall period should be the interview date. The recall period is commonly set at around 90 days.

Average household size and percentage of children under 5 years In nutrition surveys, although children are the primary target, it is households which are selected, hence the necessity for calculating the number of households and estimating the number of children and vice versa. This calculation requires knowledge of the average household size and the proportion of children below 5 years.

Non-response rate Non-Response Rate (NRR) accounts for households that could be either absent, not accessible, refuse to be surveyed, or any other reason that prevent survey teams from surveying a selected household. The sample size is accordingly inflated using this NRR.

Procedures for sample size calculation In calculating the sample size for anthropometry, the following formula is applied by ENA for SMART in automatic calculation to determine the number of children required:

Anthropometry Simple/systematic sampling

Sample size (n) = z² x p x q d²

where: z =critical value for the normal distribution at 95% Confidence Interval (C.I) = 1.96 p:=estimated prevalence (as a decimal) q=1-p d=precision (as a decimal)

Cluster sampling

Sample size (n) = t² x p x q X DEFF d² t=2.045 (Note that the t-distribution is used for cluster sampling).

The number of children is then converted to the number of households using the following formula, which assumes that children 6-59 months constitute 90% of all children below 5 years:

Sample size households (N) = Sample size children (n) (Average household size x % children under 5 x 0.9)

Example of determining the number of clusters.

The sample size for anthropometry was done on ENA for SMART software planning screen and was based on the following assumptions; . i) Average Household size 6.1 (SHHS 2006) ii) Under five children 16.7%. = 1.02 under fives in each household iii) Children 6-59 months 17.9% = 1.09 under fives in each household. iv) Estimated prevalence – 20% Realistic estimate based on Rapid assessments throughout 2006 to 2011. Couldn’t use SHHS 2006 since the time is too long before. v) Desired precision = 4; Reason: There is a plan to repeat the survey during the post harvest of 2012 for comparison purposes.

vi) Design effect = 1.5 (DEFF for malnutrition usually falls between 1.4 and 1.8) vii) Percentage of none response households = 3 based on reports of rapid assessments that have shown zero refusals. viii) Therefore anthropometric sample - Number of children to be included = 669 children. - Number of households to be included = 702 households.

Calculation of cluster size.

i) Working day from 7 am to 7 pm = 12 hours ii) Time taken to and from the field on average = 1.5 hours (90minutes). iii) Time to sample and identify 1st household = 30minutes iv) Time for lunch and breaks = 1hour (60 minutes) v) Time to interview once household = 25 minutes. vi) Time to walk from one household to another = 5 minutes. vii) Calculation. - Total working hours = 12 x 60 = 660 minutes. - Total time spend in the field undertaking various survey tasks = 90min + 30min = 120minutes. - Time available for the teams to undertake the survey = 660 – 120 = 540minutes - Therefore, number of households to be visited / day = Time available for the team divided by time spend interviewing and moving from one household to another= 540/30 = 18 households. viii) Calculation of number of clusters per survey = Sample size1 (Household) ÷ size of cluster = 713/18 = 40 clusters.

Note: This is an hypothetical example and parameters may differ case by case depending on the situation.

Mortality

Systematic random sampling The sample size (households) is calculated as follows, whereby CDR is the estimated Crude Death Rate: n = z² x (CDR) d²

Cluster sampling

11 The higher of the sample among Anthropometry and Mortality is used for the calculation of number of clusters per survey. In this case 713 households for the mortality.

n = DEFF x t² x (CDR) d²

To calculate the sample size, the parameters explained in Section 4.4 are entered into the planning screen of ENA for SMART. For ease of understanding, we will use a single hypothetical population and apply the three sampling methods separately to show how the sample size will be calculated.

4.4 Simple/systematic random sampling Example: Location A with a population of 50,000 people and 10,000 households: Estimated prevalence of GAM: 20% Estimated CDR: 2/10,000/day Desired precision- 5% anthropometry Desired precision 0.75 Recall period: 90 days % children under 5: 20% Average household size: 5 % of non-response households: 10% Recall period: 90 days

The calculated sample size is 304 households and 246 children for anthropometry and 337 households (corresponding to 1,518 population) for mortality (Figure 4). Given that, in reality, the anthropometry and mortality data is collected from the same households in a single survey, the higher number of households is taken as the sample size (337 in this case) to ensure that the sample is sufficient for both. It is important to Figure 4 Sample size calculation (SRS) note that survey teams should collect data from all 337 households even if the 246 children are measured before all the households are completed. This ensures that survey teams do not select households only with children, which can introduce bias. This method, known as the fixed household method, is also useful as nutrition surveys frequently include the collection of household-level data such as food security, which requires all households to be included for a more representative picture.

Simple random sampling In terms of implementation, with simple random sampling, the next stage is to randomly select the 337 households from the 10,000 households, using the random number generator, also found on the ENA for SMART planning screen, by entering the range of required households (1- 10,000) and the number required (337) and clicking “Generate Table” (Figure 5) to produce a list of randomly selected households. These numbers should then be sorted in ascending order. From the list of households, the households are then selected. If, for example, the first number on the list is 964, meaning that, from the list of households, the 964th household will be the first to be selected.

Systematic random sampling The total number of households (10,000) is divided by the sample (337) to determine the sampling interval (30 rounded to the nearest whole number in this case). A random number is then selected between 1 and 30 to determine a starting point (using the procedure described above). Assuming the random starting point is 5, the survey team will go to the 5th household as per the layout. This is the first household that will be interviewed. The next household will be determined by adding the sampling interval, which will be the (5+30)th household, which will be the 35th household. The next household will be the (35+30)th, which is the 65th household. This process is repeated until the 337 households are achieved.

Figure 5 Random number generator

Random Number table

Range: 1 to 10000, Number: 337

964 5461 1320 4544 8169 5635 1270 1456 7280 7644 2105 2098 5471 6759 4630 298 8404 1340 8435 412 7369 2932 4219 8311 1124 9839 3543 9534 3528 9653 3363 1319 556 8712 5178 7722 2795 8945 1144 9559 2006 5082 1993 3377 2864 7696 4101 6176 3444 35 5556 5104 571 9130 3555 3844 8612 4137 7989 9897 2810 7386 8555 9512 755 6097 5227 9443 8079 5245 4455 7279 6978 4535 5906 4558 8645 4958 7115 9381 3221 2891 5874 7747 3338 2220 2718 5324 9870 7449 8330 3885 968 3256 3230 9118 7985 8489 9581 9028 4373 5036 7462 4659 6157 4569 435 7344 5078 8615 7780 9216 7986 9921 3207 6175 8597 5597 2422 5059 188 4981 4359 344 7726 3451 9202 1327 44 2761 7828 2182 9603 329 1272 1165 2036 3577 470 9584 4521 9113 9511 650 4421 7778 256 9433 3432 7283 4890 2516 226 7277 640 8437 7039 4189 3361 2517 8195 5693 9226 2169 1009 7128 6225 1277 2064 4735 9700 4740 4310 3037 8629 6777 2965 6216 1887 9088 682 5826 6187 7458 4953 9689 9938 7901 9319 9725 5949 3659 5709 8636 5153 2099 5835 8154 3425 8066 4718 7831 8278 5398 381 9466 4589 5412 1558 4999 959 3082 481 9852 3273 5609 5922 6549 4078 1623 953 8672 3502 2111 6500 8335 5226 2199 1735 5741 4691 3089 983 880 9445 4963 4899 3245 5551 8599 7149 7585 6318 5879 1348 454 4436 7347 3198 2790 4236 393 5801 8358 25

3301 4173 8621 417 5140 9843 3046 1427 7140 7218 42 3175 525 1434 7846 7126 1536 5717 183 7381 8848 7964 1051 8354 8419 9949 3413 1296 3473 318 7688 9541 2627 3650 1777 5128 6258 254 6804 9067 3866 7335 4813 9193 191 3955 5456 2978 6180 262 4019 7045 3411 3961 5629 7583 9482 2993 8389 7827 7496 5761 8917 7558 6017 1949 9001 9896 8306 1394 1393 3403 2250 6468 81 6207 6896 1930 4011 6249 5552 9829 8451

4.5 Cluster sampling ENA calculation With the same example, to determine the sample size for cluster sampling, a DEFF must now be entered in the planning screen of ENA. The population sizes of the smaller units must also be entered to generate clusters. Let us use a DEFF of 1.5 and the sub-populations in Table 3.

Table 3 Cluster sampling example Geographical unit Estimated total population Location 1 5,000

Location 2 6,000

Location 3 6,500

Location 4 6,200

Location 5 7,000

Location 6 5,900

Location 7 7,100

Location 8 6,300

Total 50,000

A decision must then be made as to the number of clusters to be used for the survey. The higher the number of clusters, the higher the probability will be that the sample will be truly representative of the population from which it is selected. This is because, the more clusters there are, the smaller the confidence interval will be around the estimate of the prevalence of malnutrition and the more accurate our estimate of malnutrition will be. According to SMART guidance, the number of clusters are recommended to be 30 or more, and cannot go below 25 under any circumstanced. In this example, let us assume that we will use 30 clusters. The output from ENA for SMART, using the same assumptions above, gives 551 households and 401 children. In this example, we will use 30 clusters. After entering the variables in ENA for SMART, and clicking “Assign Cluster’, the clusters are selected from the population sub-units. By clicking the icon , the table with the selected clusters is copied to Microsoft Excel (Figure 6).As shown, the 30 clusters have been selected, meaning that location one will contain clusters 1 to 3 and so on. Note that the software also generates replacement clusters (RC) which are substitute clusters to be used in the event that 10% or more of the clusters are unreachable, either due to insecurity of other reasons. In such circumstances, all the RCs will be used. 26

Step 2

Step 1

Figure 6 Sample size calculation (Cluster sampling)

Output: Geographical Population unit size Cluster Location 1 5000 1,2,RC,3 Location 2 6000 4,RC,5,6 Location 3 6500 7,8,9,10 Location 4 6200 11,12,13,14 Location 5 7000 15,16,17,18,19 Location 6 5900 20,21,22,23 Location 7 7100 RC,24,25,26,27 Location 8 6300 28,29,30,RC

Having selected the clusters, the next stage is to select households. In this example, the sample required is 551 households, meaning that there will be 551/30=18.4 children per cluster. This will be rounded up to 19 (it is advisable to round up rather than down). The 19 households within each cluster must be randomly selected. The recommended approach is to use simple random sampling of households from a list, or systematic random sampling if a list is unavailable and households are arranged in a clear pattern. It may be that villages are not very large but village leaders are still unable to list all households. In such situations, survey team members can walk around the village and identify all households, by writing a number (starting at 1 to the total number of households in the village) with a chalk on their door, for example. If clusters have been selected from villages, the list will be obtained from the village leader. Only in the event that the list of households is unavailable, the modified EPI method is used.

Figure 7 Modified EPI method

When the team arrives at the village that will contain the cluster, the following procedure should be followed after discussions with the village leaders (Figure 7).

 Go to somewhere near the centre of the selected cluster area.  Randomly choose a direction by spinning a bottle, pencil, or pen on the ground and noting the direction it points when it stops.  Walk in the direction indicated, to the edge of the village. At the edge of the village spin the bottle again until it points into the body of the village. Walk along this second line counting each house on the way.  Using a random number list select the first house to be visited by drawing a random number between 1 and the number of households counted when walking. For example, if the number of households counted was 27, then select a random number between one and 27.  The team will not have a computer in the field, so each day before setting out, the supervisor should print out the list of random numbers. If the number 5 was chosen, go back to the fifth household counted along the walking line. This is the first house you should visit.  Go to the first household and interview all children aged 6–59 months in the household for the nutritional survey and complete the mortality questionnaire.  The subsequent households are chosen by proximity. In a village where the houses are closely packed together, choose the next house on the right. 28

 Continue in this direction until the required numbers of households are interviewed.  Continue the process until the required number of children has been measured.

The modified EPI method understandably gives less representativeness as children will be selected with close proximity to each other, which has potential for bias. Additionally, the selection of a household is actually determined by the selection of another, which contradicts the principles of probability sampling.

Segmentation In some cases, villages selected randomly to contain a cluster might be very large or households very dispersed and sample selection can then become very tedious; teams will have long distances to walk and not enough time to complete one cluster per day. In those scenarios (approximately more than 100 households in the village), segmentation can be used in order to reduce the area that will be covered by the survey teams. The objective of this procedure is to divide the village into smaller segments and choose one segment randomly to include the cluster. This division can be done based on existing administrative units, such as natural landmarks (river, road, mountains, etc.) or public places (market, schools, churches, mosques, temples, etc.) The segments should be preferably be of approximately equal size, whereby the team will randomly select one segment to be the cluster. This should be accompanied by a sketch map.

4.6 Adjustment for small sample size If the target population (number of children 6-59 months) is below approximately 10 000, the “Correction for small population size” box is clicked in ENA for SMART (Figure 7 and 8). This is because, If the target population is small, a smaller sample size would be needed to achieve the required precision. ENA for SMART calculates the target population from the total number entered in the cluster selection table and % of Under-5 children entered into the calculator. For example, if the total population size in the cluster selection window is 40,000, and % of under-5 is 15%, ENA for SMART would assume that there are 40,000x0.15x0.9=5,400 children aged 6- 59 months in this sampling universe, and use this number for adjustment for small population size Figure 8 shows the effect of adjusting for small population size. With the same assumptions, the required sample size has reduced from 496 to 485 households for anthropometry.

5. SURVEY FIELD PROCEDURES Figure 8 Adjustment for small population size This section will highlight ways of ensuring that field teams are well managed, and will discuss measures for reducing bias during data collection. Special field circumstances in sampling and selection will also be explained.

5.1 Bias Bias is anything other than sampling error that causes the results of the survey to be different from the actual population prevalence. Bias cannot be calculated nor its effect upon the result assessed, but is the main reason for inaccurate survey results. It is important for survey teams to understand potential sources of bias and to minimise them.

Common sources of bias in nutrition surveys include:  Systematic errors due to faulty weighing equipment or measuring techniques.  Non-calibration of weighing equipment.  Recall error: Respondents often fail to recall all deaths during a given recall period. Infant deaths, in particular those within a short time after birth, are particularly under- reported. Respondents may also misreport ages, dates, and salient events.  "Calendar" error: Respondents may report events as happening within the recall period when they did not (or vice versa) due to lack of clarity about dates.  Age heaping‖/digit preference: Respondents may round ages to the nearest year i.e. 12, 24, 36 and 48 months.  Sensitivity/taboos about death: In general, the death of a household member is not a subject discussed readily with strangers.  Deliberate misleading: In some populations, with experience of relief operations, some respondents may deliberately give incorrect answers in the expectation of continuing or increased aid.  Interviewer error: Enumerators may ask questions or write down answers incorrectly, skip questions, assume answers, or rush respondents in an effort to complete the interview quickly.

The best way to minimise bias is to thoroughly train and supervise teams, ensuring that all procedures outlined in section 6 are strictly followed. Additionally, the following minimise bias and enhance accuracy of data:  Ensure errors in the field are minimised by using good quality equipment that is regularly calibrated.  Check the questionnaires and control forms for blank entries at the end of each day to make sure no data is left out. The team leader should review all questionnaires before leaving an area in order to make sure no pieces of data have been left out. If there are any problems the team can return to the household and correct any identified error.  Check for data collected. Each evening, or during the next day while the teams are in the field, the supervisor should arrange for data to be entered into the computer. Recording errors, unlikely results, and other problems with the data may become clear at this stage. ENA software will automatically flag abnormal values as data are entered.  Each morning, before the teams set out for the day, there should be a short feedback session. If any team is getting a large number of flagged‖ results, the supervisor should accompany that team the next day. If the results are very different from those obtained by the other teams, it may be necessary to repeat the cluster from the day before.

5.2 Supervising data collection team Field supervision is important in ensuring valid data collection and minimising bias. The supervisors should:  Make frequent unannounced spot checks on the teams in the field.  Ensure that the methodology is closely followed and document any deviations.

 Check all questionnaires and control forms to ensure that all sections are accurately completed.  Ensure that all instruments to be used the survey teams are calibrated every day.  It is particularly important to check cases of oedema, as there are often no cases seen during the training and some team members may therefore be prone to mistaking a fat child for one with oedema.

5.3 Special circumstances There are certain circumstances which field team may encounter, which must be anticipated. Some households or children may be absent or refuse to participate, and in some cases there may be need for teams to return to the households later in the day. All these must be documented, and it is strongly recommended to use a cluster control form for this purpose (Annex 10).

Impossible to visit a selected household In the event that a household cannot be interviewed, either due to refusal or lack of access, the team should continue to the next household according to the sampling procedure. The households that are impossible to visit have already been accounted for in the planning stage by inflating the sample size with the non-response rate.

No children in the household Not all households will have children. All applicable modules/questionnaires should be completed if there are no children. Excluding households without children from selection will introduce serious selection bias in measuring household-level and other non-child variables (e.g., mortality, WASH, food security).

Absent household The survey team may find all household members absent. After confirming with neighbours, this should be recorded on the cluster control form. The team should return to absent households before leaving the village, to see if residents are back. If not, this should be reported on the questionnaire and control form. As explained above, absent households are not replaced.

Absent household This is a household which has had no one living there for a long time. Such households should not be included in the list of households used for household selection.

Absent household If a child lives in the household but is not present at the time of the survey, this should be recorded on the household questionnaire and control form, and the household should be revisited before the end of the day. The rest of the information (age, sex, feeding practices, immunizations, etc.) can still be filled completed.

Child with disability Some disabilities might not allow you to take all anthropometric measurements needed or might lead to a biased measure. For example, the weight of a child missing a limb will not be very meaningful when comparing it with the standard population. All other data that is not influenced by the disability should be collected such as sex, age, oedema (if the child has both feet), etc should be collected.

Child in feeding centre/clinic

Such children should be recorded as absent.

6. SELECTION AND TRAINING OF SURVEY TEAMS This section will outline guidance for selecting survey teams, and the aspects to be included in training of enumerators.

6.1 Selection of survey teams Survey team members do not necessarily have to be health professionals. However, They need to be fit, as there is usually a lot of walking. They should have a relatively high level of education, as they will need to read and write fluently, and count accurately. Ideally, they will speak the local language. If not possible, there should be interpreters as part of the survey teams. Women generally have much more experience dealing with young children and should usually lead the interviewing of mothers/caretakers of children. This is also important as some cultures do not allow women to be interviewed by men. The gender composition of the team should conform to the local context.

The composition of survey teams depends on the data to be collected. Two people are required for measurement of children (measurer and recorder) in addition to an interviewer. A team leader is also required for quality control and leadership of the survey team. If there are additional modules such as food security and water and sanitation, which are household modules, an additional member may be required.

6.2 Training of survey teams Survey team members must receive adequate training prior to conducting a survey, even if they have prior survey experience. Each survey has its unique challenges, and survey work requires constant re-training so as to standardize methods and techniques as well as to update knowledge. The survey manager must come up with a survey training schedule and organise a suitable training venue, where there is sufficient space. The equipment and materials for the training need to be procured and organised in advance of the training.

The main topics to cover in training of data collectors include:  Introduction to nutrition surveys: To introduce team members to the rationale behind surveys and the objectives.  Sampling procedure: Defining sampling, explaining why sampling is used and how it is applied. Describe the rationale and importance of representativeness and outline the sampling method to be used for the survey.  Interviewing techniques and questionnaires: Explain the best practice in terms of interviewing so as to prevent bias. Go through each survey question to give guidance on

clarity, answer options, cultural appropriateness, and gender sensitivities, to avoid suggestive questioning but probe where necessary.  Measurement techniques: Introduce the teams to the different measurements to be used in the survey and the equipment to be used, and explain procedures for each. This is followed by a standardisation test exercise at the end of the survey for anthropometry.  Composition of survey teams, roles and responsibility of team members: Discuss and agree on the composition of the different teams and assign team leaders and supervisors, clearly explaining the responsibility of each.  Survey field procedures: Go through the procedures to be followed by teams before going to the field, whilst in the field, and after leaving the field on a daily basis.  Survey logistics: Outline the plans for the survey regarding: materials and equipment to be used by each team, communication, travel, food and accommodation in the field as well as allowances (if applicable).

6.3 Roles and responsibilities of team members The roles of the different members of the survey team are generally as follows:

Survey manager (1 per survey) 1. Gathering available information on the context and survey planning. 2. Selecting team members. 3. Training team members. 4. Supervision of the survey: Taking necessary actions to enhance the accuracy of data collected. 5. Visiting teams in the field and making sure that supervisors are following up team leaders. 6. Ensuring that households are selected properly and, that the equipment is checked and calibrated each morning during the survey, and that measurements are taken and recorded accurately. 7. Deciding on how to overcome the problems encountered during the survey. Each problem encountered and decision made must be promptly recorded and included in the final report, if this has caused a change in the planned methodology. 8. Organizing data entry into ENA for SMART and checking any suspect data every evening, by using the appropriate sections of the SMART plausibility report. 9. Organizing an evening or morning “wrap-up” session with all the teams together to discuss any problems that have arisen during the day. 10. Ensuring that the teams have enough time to take appropriate rest periods and has refreshments with them. It is very important not to overwork survey teams since there is a lot of walking involved in carrying out a survey, and when people are tired, they may make mistakes or fail to include more distant houses selected for the survey. 11. Analyse and write the report.

Survey supervisor (1or 2 per team depending on survey) 1. Visiting teams in the field and making sure that before leaving the field, each team leader reviews and signs all forms to ensure that no pieces of data have been left out; making sure that the team returns to visit the absent people in the household at least twice before leaving the area. 2. Checking cases of oedema, as teams are prone to mistaking a fat child for one with oedema (particularly with younger children). 3. Ensuring all food/refreshments are ready at the start of the day.

4. Participating in deciding on how to overcome the problems encountered during the survey. Each problem encountered and decision made must be promptly recorded and included in the final report, if this has caused a change in the planned methodology. 5. Assisting with organizing data entry into ENA for SMART and checking any suspect data every evening, by using the appropriate sections of the SMART plausibility report and other checks.

Team leader (1 per team) 1. Ensuring that all questionnaires, forms, materials and equipment are ready at the start of the day. 2. Calibrate measuring instruments on a daily basis. 3. Organising briefing meeting with team before departure in morning. 4. Speaking with representatives to explain the survey and its objectives. 5. Using a local events calendar to estimate the age. 6. Checking if the child is malnourished and making a referral if necessary. 7. Supervising the anthropometric measurements. 8. Ensuring that households with missing data are revisited before leaving the field the same day. 9. Ensuring that all equipment is maintained in a good state. 10. Managing time allocated to measurements, breaks and lunch. 11. Ensuring the security of team members. 12. Noting and reporting problems encountered to the supervisor.

Measurer (anthropometry) -1 per team 1. Measuring weight, height, weight and MUAC. 2. Assessing the presence of oedema.

Assistant (anthropometry) -1 per team 1. Using local events calendar to estimate age. 2. Recording age. 3. Ensuring each child is in the correct position for measurement. 4. Recording the measurements on the questionnaire.

Interviewer -1 per team 1. Conducting interviews. 2. Completing the questionnaire. 3. Documenting missing data and making re-visits where necessary.

6.4 Standardization test The standardization test consists of all the members of the teams measuring 10 (or more) different children twice, with a time interval between individual measures. The size of the variation between these repeated measures is calculated to assess how precisely each person measures the children (repeatability of measurements).

The standardization exercise is performed with a group of children whose ages fall within the range for the study (6–59 months). Before carrying out the exercise, the supervisor carefully measures each child without allowing the trainees to see the values. The supervisor is automatically given the ID number 0, and should start by filling in the form. It is important that the supervisor undertakes the exercise as well as the team members. The supervisor’s data may 34

be assumed to be of higher quality than the trainees; however the actual values should relate closely to the mean value for all the teams.

Each team member is also given a unique ID. Each child that will be measured is also be given a child-ID, starting from 1. For the exercise, each child, with his/her mother, remains at a fixed location with the ID number clearly marked. The distance between each child should be far enough to prevent the trainee from seeing or hearing each other’s results.

At the beginning of the exercise, each pair of trainees starts with a different child. The supervisor instructs the measurers to begin the measurements. The trainees should carefully conduct the measurements and clearly record the results on the second, third and fourth columns of the standardization form next to the child’s identification number (Table 4a and 4b).

Table 4 Standardisation test Enumerator name...... ID...... 1st measure Enumerator name...... ID...... 2nd measure Child Weight Height MUAC Child Weight Height MUAC (kg) (cm) (mm) (kg) (cm) (mm) 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 10

Figure 9 Standardisation test

Each pair of measurers should have their own form to complete, and each should take turns taking measurements. When each member of the pair has done the measurement, they should move on to the next child. At the end of the process, the sheets are handed in and a second sheet is taken. The teams then take a break (lunch). The whole process is then repeated after the break. Thus, without seeing the measurements they previously made, each enumerator measures each child twice.

The equipment used in the exercise should be the same equipment used to measure children in the survey itself. The team members will rotate but the equipment should not, so that each child is always measured with the same equipment (the team is being tested not the equipment). Only one pair of measurers should be with a child at any one time. Talking between pairs of trainee measurers during this exercise should not be allowed.

Upon completion of the exercise, the data is entered into the “Training” screen of ENA for

SMART, as shown in Figure 9. On clicking , a report is generated in Microsoft Word, giving the accuracy of each measurer either as “good” or “poor”.

Each team member’s measurements are compared to the mean of the whole group to assess how accurately the measurements are made. Each team member is then given a score of competence in performing measures. Any misunderstandings or errors in technique are corrected during the training. Any team member unable to make the measurements sufficiently well should be replaced or given a different job in the survey that does not require taking the primary measurements.

6. DATA COLLECTION .

This section will explain the procedures for data collection for anthropometry, mortality as well as additional indicators, which include morbidity, infant and young child feeding.

7.1 Anthropometry Introduction to anthropometry Anthropometric measurements (measurements of body proportions, such as weight and height) are used to give an approximation of the nutrition status of a population, or to monitor the growth and development of an individual. At the individual level, anthropometric data is used to determine whether or not an individual is malnourished. In turn, this information may be used to decide whether or not the individual should be included in a supplementary feeding programme, or treated for severe malnutrition. The information is also used to decide when to discharge the individual from a feeding programme. At the population level, anthropometric data is used either in a one-off survey to assess what proportion of a population is malnourished, or as a surveillance tool to follow the nutritional situation of a population. Collectively, the anthropometric measurements of children aged 6-59 months may be used to compare different populations, or to make comparisons of the same population over time. Anthropometry data is mandatory for all nutrition surveys as it forms the basis for determining the magnitude of malnutrition.

Nutrition indices and indicators When body measurements are compared to a reference value they are called nutrition indices. Three commonly used nutrition indices are weight-for-height (WFH), weight-for-age (WFA) and height-for- age (HFA). The indices are shown in Figure 10. As an alternative to weight-for-height (WFH), wasting can also be measured by Mid Upper Arm Circumference (MUAC), which is relatively easy to measure and a good predictor of immediate risk of death for children 6-59 months. It is used for rapid screening of acute malnutrition. MUAC can be used for screening in emergency situations but is not typically used for evaluation purposes

Figure 10 Indices of nutritional status. Source: ENCU/PPDA (2002).Guideline on Emergency Nutrition Assessment

Nutrition indicators are an interpretation of nutrition indices based on cut-off points. Whereas indices are simply a figure, indicators represent an interpretation of the indices. For example, WFH is an index of nutritional status, whereas low WFH is an indicator. Anthropometric indices or indicators are most commonly expressed as z-scores. A z-score is a measure of how far a child’s measurement is from the median value of the reference distribution.

For example, weight-for-height z-score (WHZ) is based on: a) The child’s weight b) The median weight for children of the same height and sex in the reference population c) The standard deviation for the distribution of weights in the reference population for 39

children of the same height (because the standard deviation of a distribution increases as children get older, you need to use the standard deviation for the reference distribution of children of the same height).

WHZ = actual weight – median weight standard deviation for reference population

The median values and standard deviation are contained in the reference population. The recommended reference to use is the In WHO 2006 reference (Annex 1), although when data is analyzed by ENA for SMART, results are also presented using the NCHS 1977 child growth standards for purposes of comparison only in the annex.

Classification of nutritional status Chronic malnutrition, underweight and wasting A z-score below -2 for any indicator defines moderate malnutrition, whilst a z-score below -3 defines severe malnutrition. For example, a WHZ below -2 is classified as moderate wasting, whilst a WHZ below -3 is classified as severe wasting.

Acute malnutrition Acute malnutrition is defined either by the presence of oedema or WHZ below -2 or MUAC below 125mm. The classification of acute malnutrition according to the World Health Organisation (WHO) is shown in Table 5. Note that, in addition to wasting, oedema is used to classify acute malnutrition. Oedema is a clinical sign of acute malnutrition, defined as presence of excessive amounts of fluid in the intracellular tissue. In identification and referral of children for acute malnutrition, MUAC are both used, in addition to oedema.

Table 5 Classification of acute malnutrition Indicator Weight-for- Weight-for- Mid Upper Arm Oedema Height z-score height % of Circumference (MUAC) (WHZ) median in mm (WHM) Moderate acute -2> WHZ >= 70%>WHM 125> MUAC >= 115 No malnutrition (MAM) -3 >=80% Severe acute WHZ < -3 <70% MUAC <115 Yes/No malnutrition (SAM) WHZ >-3 >70% MUAC >115 Yes Global acute WHZ<-2 <80% MUAC<125 Yes/No malnutrition (GAM)

The percentage of median is an alternative, though not as commonly used, classification. With this classification, the calculation is:

Observed value X 100% Median value The cut-off for moderate malnutrition is less than 80% of the median, whilst the cut off for severe malnutrition is less than 70% of the median. Children who are found to be malnourished according to the above classification should be referred to the nearest health facility using a referral form (Annex 3).

Estimation of age In order to calculate the z-scores for WFA and HFA, the age of the child must be calculated in months. In an ideal scenario, the age is taken from the child health card/vaccination card, or in some cases, the birth certificate. However, in some contexts, official age documentation is unavailable, in which case the survey team must rely on the recall of the mother or primary caregiver of the child to 40

provide an estimate of the date of birth. In certain cases, the mother/caregiver may have forgotten the exact date of birth. In such as case, the recommended method is the local calendar of events

The local calendar of events (Annex 2) shows the dates on which important events took place during the past five years. It can show local and national holidays, religious holidays, and other major events such as elections, and also has information on seasons (winter, summer, dry season, wet season, etc.). These events can help identify which month the child was born in. The use of such a calendar can be time consuming. However, in addition to estimating age, the local calendar of events is a very useful tool in determining whether children should be included in a nutrition survey by checking the ranges of the age of a child.

Procedures for measurement In anthropometric measurement it is important for the survey manager to adequately train survey teams to ensure strict adherence to procedures of measurement. Slight deviation in measurements due to errors in reading measurements may result in misclassification of malnutrition, thereby leading to wrong conclusions being drawn on a survey population. The commonly used equipment is shown in Figure 11. Weight is measured using either a Salter hanging scale or electronic mother-to-child scale which read values to the nearest 0.1kg. It must be noted that the electronic scale has a higher precision than the salter scale and is therefore preferred. Challenges have been observed with using the salter scale, particularly with the readings, which are subject to bias due to inaccurate readings, especially when the measured child is restless. Electronic scales are not subject to such limitations. Height is measured using a height board to the nearest 0.1cm, and MUAC is measured using a MUAC tape to the nearest mm or cm depending on the MUAC tape used.

Figure 11 Anthropometric equipment

Salter hanging scale Mother-to-child scale Height board MUAC tape

Figure 11 Anthropometric equipment

Weight measurement a. Using Salter hanging scale(Figure 12) The following steps should be followed 1. Explain to the child’s mother/caregiver what you are going to do. 2 Hang the scale from a suitable point such as a tree, doorframe, and make sure that the dial on the scale is at eye level. 3 Hang the weighing pants from the hook of the scale and check that the needle reads zero. 4 Remove the child’s clothes and any jewellery, and place him or her in the weighing pants. 5 Hang the weighing pants, with the child in 41

them, from the hook on the scale. 6 Check that nothing is touching the child or the pants. 7 Read the scale at eye level to the nearest 100 g (0.1kg). 8 Measurer reads out measurement. 9 Assistant repeats and records. Figure 12 Salter hanging scale b. Using mother-to-child scale (Figure 13) Children who are able to stand on their own (usually 2 years and above), the procedure is as follows:  Explain the procedure to the mother/caregiver.  Switch on the scale.  Ask the mother/caregiver to remove the child’s clothes.  When the scale reads 0.0, the child may stand on the scale with the feet in the position indicated on the scale markings.  Read the measurement to the nearest 0.1kg.  Measurer calls out reading.  Assistant repeats the reading and records the reading on the questionnaire. For children unable to stand, the procedure is as follows  Switch on the scale.  When the number 0.0, request the mother/caregiver to remove her shoes and step on the scale to be weighed alone. Figure 13 Mother-to-child scale  Ask someone to undress the child and wrap him/her in a blanket if necessary.  Ask the mother/caregiver to stand on the scale in the position marked on the scale.  Tare the scale by pressing on the position indicated whilst the mother/caregiver remains on the scale.  When the scale displays 0.0, hand the baby to the mother/caregiver, requesting her to remain still.  Measurer calls out the weight that appears to the nearest 0.1kg.  Assistant repeats and records the measurement on the questionnaire.

Weight measuring scales must be calibrated on a daily basis during nutrition surveys by measuring standard 5kg weights on a daily basis and recording the measurement so as to detect problems which may cause bias and affect the representativeness of the survey results.

Height measurement Children below 24 months (or below 87cm when age cannot be determined) are measured lying down, whilst children 24 months and above (or 87cm and above if age cannot be determined) are measured standing up (Figure 14).In the event that a child aged above 24 months (or 87cm) is measured lying down, maybe in the case where the child is unable to stand, a correction factor is applied, whereby 0.7cm is subtracted from the height. The following procedure is followed 1. Explain procedure to mother/caregiver. 2. Remove child’s shoes or any hair ornament. 3. Place child on board, watching out for the essential points (head, shoulders, buttocks, knees, calves) and that the moving piece is on the feet for measurements lying down and on the head for measurements standing up. 4. Measurer reads out measurement to nearest 0.1cm. 5. Assistant repeats and records the measurement on the questionnaire.

Figure 14 Height measurement

MUAC measurement (Figure 15) The following steps should be followed in measuring MUAC: 1. Locate the midpoint of the child’s left upper arm by first locating the tip of the child’s shoulder and elbows with the child’s elbow bent and measuring and dividing by 2. 2. Measurer: Straighten the child’s arm and wrap the tape around the arm at midpoint making sure the tape is not too loose or tight 3. Measurer reads and calls out the measurement to the nearest 0.1cm. 4. Assistant repeats then records the measurement on the questionnaire.

Figure 15 MUAC measurement

It is important to ensure that the measuring tape is of appropriate quality and that the arm is in the correct position. The tape must neither be too loose, nor too tight.

Nutritional oedema (Figure 16a) Oedema is identified by applying moderate finger (both hands) pressure on top of feet. The measurer then maintains pressure for 3 seconds: (Two thousand and one, two thousand and two, two thousand and three) then removes his/her thumbs. If an impression remains on both feet, then there is oedema.

Figure 16a Identification of nutritional oedema

Figure 16c Nutritional oedema grade 3 Figure 16b Nutritional oedema grade 2

Table 6 Classification of nutritional oedema

Observation Classification No oedema Grade 0 In both feet (below ankles) Grade 1 In both feet and legs (below knees) Grade 2 In both feet, legs, arms, face Grade 3

As shown in Table 6, nutritional oedema is classified as grade 0 (no oedema), grade 1 (both feet and below ankles), grade 2 (both feet and legs below knees as shown in Figure 16b) and grade 3 (both feet, legs, arms, face as shown in Figure 16c). The observation is recorded on the questionnaire for anthropometry (Annex 4).

7.2 Mortality At the start of an emergency, the assessment of mortality is highly recommended as survey data may be the only source of information on death rates in the recent past. An elevated death rate can indicate that there is a health problem in a population, but it cannot indicate the cause.

To determine the death rates, each member of each selected household is interviewed to obtain information on births, deaths, in-migration and out-migration of all household members present for at least some part of the recall period. The recall period (RP) is a specified period in the recent past. In emergency, the recall period is usually set as 90 days. However, it must depend on the period during which events can be accurately reported by respondents. A shorter recall period gives a more accurate estimate of mortality, as events which are more distant are more likely to be forgotten. However, a longer recall period gives a more precise estimate of mortality (a narrower confidence interval as the sample size will be greater). Each day, each member of the household is at risk of death, although very few actually die on any given day. The actual deaths are expressed in relation to the number of people and the length of time they were at risk. We need to find out how many people have been at risk during the recall period, and therefore household members who have left the household should be counted. Similarly, those who have joined the household during the recall period have not been at risk for the whole of the recall period (Figure 17).

Figure 17 Death rate calculation

Death rate (in deaths per 10,000 per day) is calculated using the formula (Figure 18):

Figure 18 Death rate formula

Death rate is expressed as Crude Death Rate (CDR): the number of people in the population who die in a specified period of time, and 0-5 Death Rate (0-5 DR), which is the number of children aged from birth to 5 years who die over a specified period of time in relation to the total number of children below 5 years of age in the population. The procedure for collecting the data is as follows (see Annex 5 for the Mortality questionnaire): 1 List all the household members at the time of the survey and indicate whether each of these household members were present at the start of the recall period. 2 List all members of the household that were present at the start of the recall period but have left the household during the recall period. 3 Indicate whether the individual is above or below age 5 (to derive 0–5DR) and the sex (only is sex-specific death rates are required). 4 Indicate the births that occurred during the recall period. 5 Indicate all deaths that occurred in the household during the recall period.

The data is summarised in the format in Table 7, which is the same format as the ENA for SMART “Data entry household level” screen for death rate calculation.

Table 7 Mortality rate calculation summary Current HH members (total)

Current HH members (below 5 years)

Current HH members who arrived during recall (exclude births)

Current HH members who arrived during recall (below 5 years)

Past HH members who left during recall (exclude deaths)

Past HH members who left during recall (below 5 years)

Births during recall

Deaths during recall (total)

Deaths during recall (below 5 years)

The questionnaire is shown in Annex 5.

7.3 Additional indicators According to UNICEF’s conceptual framework of malnutrition (Figure 19), there are certain individual and household-level indicators which cause malnutrition. At the individual level, morbidity and insufficient dietary intake are immediate determinants of malnutrition. It is therefore relevant to investigate morbidity and infant and young child feeding in nutrition surveys. At the household level, food insecurity, inadequate maternal and child care, as well as inadequate health services and environment are underlying causes. Food security and water and sanitation are therefore frequently studied in nutrition surveys.

Figure 19 Conceptual framework of malnutrition

Morbidity, measles vaccination and Vitamin A supplementation Additional individual-level indicators are:

Retrospective morbidity for children 6-59 months for the past 2 weeks: the proportion of children who suffered from fever, cough or diarrhoea. The additional individual-level indicators are integrated in module 1 (Child nutrition and health module) in Annex 4.

Infant and young child feeding (IYCF) IYCF indicators The target group for IYCF is the 0-23 month (below 2 years) age group. The following core IYCF indicators should be included in every nutrition survey as defined by WHO/UNICEF (2007): 1. Early initiation of breastfeeding: the proportion of children born in the last 24 months 2. Exclusive breastfeeding: the proportion of infants below 6 months (0-5 months) of age who are fed exclusively on breast milk. 3. Continued breastfeeding at 1 year: this is the proportion of children 12-15 months of age who are fed breast milk. 4. Introduction of soli, semi-solid or soft foods: the proportion of infants 6-8 months of age who receive solid, semi-solid or soft foods. 5. Minimum dietary diversity: the proportion of children 6-23 months of age who receive foods from 4 or more groups. 6. Minimum meal frequency: proportion of breastfed and non-breastfed children 6-23 months of age who receive solid, semi-solid, or soft foods (including milk feeds for non-breastfed children) the minimum number of times or more. 7. Minimum acceptable diet: Proportion of children 6-23 months of age who receive a minimum acceptable diet (apart from breast milk). 8. Consumption of iron-rich or iron-fortified foods: Proportion of children 6–23 months of age who receive an iron-rich food or iron-fortified food that is specially designed for infants and young children, or that is fortified in the home.

The questionnaire for IYCF is in Annex 6 (IYCF Module). It must be noted that sample sizes for age ranges such as 0-8, 6-8 and 12-15 months may be quite small in the sample, and may be challenging in terms of generalising the findings. If IYCF is to be included in the survey it is important to ensure that, in addition to being representative of children 6-59 months, the sample size is representative of children 0-23 months. In order to ensure this representativeness, it is recommended to calculate the sample size based on the proportion of children 0-23 months. If this adjustments are not done, the results of IYCF will not be representative and therefore should be read with caution. Such results can only be used as an indicator of the probable situation.

Example: Let us assume that a district A has an estimated 20% children under 5 years (0-59 months) with average household size 6. Given that there are ten 6-month intervals between 0 and 59 months (0-5, 6-11, 12-17, etc.), each constituting 2% (20/10=2%). if we assume an equal age distribution, we may calculate the proportion 0-23 months as: 2 x number of 6-month intervals (0-5, 6-11, 12-17, 18- 23). This gives 2 x 4=8% children 0-23 months.

The formula for calculating sample size is: 1.962x p x q x DEFF D2 p is the estimated prevalence (of the IYCF reference indicator) in decimals q=1-p d is the desired precision of the estimate DEFF is the design effect (1 for simple/systematic random sampling)

The number of households is given as: No of children required proportion of age group x average household size

Let us assume that our parameters are as follows:  Estimated prevalence: 50%  Desired precision: 7.5%  Design effect: 1.5

The required sample size for 0-23 months would be: (1.962x 0.5x0.5x1.5/ (0.075)2= 256 The number of households would be: 256/ (0.08 x 6) = 534

This would ensure a representative sample of children 0-23 months. Given that the % below 5 years is 20%, whilst the % 0-23 months is 8%, we can calculate the proportion of children below 5 years in our sample by proportion as follows:

% < 5 years/% 0-23 months x number 0-23 months= (20/8) x 256=640

Household-level indicators The household module is optional depending on the context and is subject to resources available for the nutrition survey, the urgency or usefulness of the information and the availability of the same or similar information from other sources. Water, sanitation and hygiene (WASH) questions investigate:  Access to safe drinking water  Access to safe sanitation  Hygiene behaviour

Food security looks at:  Sources of income  Dietary diversity  Coping mechanisms 49

The Food security and WASH questionnaires are in Annex 7 and 8.Questionnaires must be administered in local language. The translated questionnaires are shown in Annex 9.

8. DATA ENTRY AND CLEANING This section will highlight the procedures and key for analyzing the quality data collected, with particular emphasis on anthropometric data.

8.1 Data entry As previously highlighted, the team leaders must check all questionnaires and control forms for completion and errors and then sign. The supervisor should then check again and sign before submitting completed questionnaires and control forms to the data entry team.

It is strongly recommended to enter data on a daily basis at the end of the day, so as to provide feedback to teams in the event that there is missing information or errors. It is strongly recommended to enter data for anthropometry in ENA for SMART, or to transfer the data from a data entry template such as Microsoft Excel. The ENA for SMART data entry screen provides the first level of data cleaning as errors are detected as soon as data is entered due to the plausible ranges (low and high) are set in the variable view section (Figure 20), which ensures that out of range values are “flagged”.

Figure 20 Variable view

In this example, after having set the range for age in months (6-59.99), the ages of two children have been recorded as 5 and 60 months in the data view section of ENA for SMART, and automatically are coloured in purple, to indicate that they are out or range (Figure 21).

Figure 21 Data view

Errors in anthropometric measurement are also determined by the flags for z-scores.Firstly, on the variable view section of data entry, the defualt values for WHZ, WAZ and HAZ are known as the WHO flags, with ranges WHZ –5 to +5, HAZ -6 to +6, WAZ -6 to +5. Values outside this range again appear in purple on the data view section (Figure 22).

Figure 22 Flagged values on data entry screen

EPI flags are based on values that are absolute abnormalities, and are therefore identified for individuals as the data are being entered. The data entry person whould start by checking such values for data entry error. After this check, the survey teams should return to the household and take the measurements again if possible. In this example, the HAZ on line 1 and HAZ and WHZ on line 4 have been flagged as outliers.

There is a second level of analysis of outliers, at the “Results for anthropometry” section of ENA for SMART (Figure 23). There are two options for exclusion of outliers: the WHO flags (previously explained) and the SMART flags. The SMART flags exclude from the analysis data that are “more likely to be errors than real values” based on the normal distribution, and automatically exclude values for WAZ, HAZ and WHZ outside the range -3 to +3.

Figure 23 SMART and WHO flags

For the first stage of data cleaning, it is recommended to use the WHO flags. However, for final analysis, the SMART flags are preferred.

In addition to analyzing outliers, ENA for SMART can be used to check for double entry when data entry is done by two different people, using the “Check of double entry” under the “Extras” section (Figure 24).The data in this case is matched by line number and must be entered in the same order.

Figure 24 Check for double entry

8.2 Plausibility check The data quality report generated by ENA for SMART is the plausibility report. Data quality should be checked during the course of the data collection, on a daily basis, and a final plausibility report must be generated at the end of data entry and cleaning to give an overall analysis of a survey’s data quality. Upon clicking the “Report Plausibility check” on the “Data Entry Anthropometry” section, a report is automatically generated in Microsoft Word (Figure 25). The first part of the report is a summary of data quality, which is followed by a detailed analysis for each indicator and for each team.

Figure 25 Plausibility report

The indicators and quality criteria are summarized in Table 8

Table 8 Plausibility report criteria Criteria Acceptable range/cut- Description off

Missing/flagged data Must not exceed 7.5% Flags are used to identify children with data out of the (preferable below 5%) usual range that are likely to be incorrect because of unlikely combinations of weight, height, age and sex data.

Overall sex ratio 0.8-1.2 The sex ratio (number of male divided by number of female) should be around 1. This verifies that both sexes are equally distributed, and hence that no selection bias has occurred and confirms the representativeness of the sample.

Overall age Different age groups If age distribution is ok, then there is no selection bias of distribution should be equally children. This confirms the representativeness of the represented. There sample and makes sure whether the sample is should be no obvious representative of the age group originally targeted for the peaks. survey.

Digit preference for There should not be any Assessing the distribution of the final decimal for height weight, height, obvious peaks of certain and weight and MUAC will tell if the survey workers are MUAC digit for weight and rounding weight and/or height measurements to the height. nearest kilogram or centimetre, respectively and thereby taking inaccurate measures. The digit preference scores should be <20

Standard deviation of SD should be below 1.2 The standard deviation for WHZ explains the dispersion WHZ (weight-for- z-scores. of z-score values around the mean. If SD>1, the height z-score) distribution is more dispersed than the standard population. If SD<1.0, the distribution is less dispersed than the standard population. This is the most common test done to assess the quality of the anthropometric data. The ispersion should be close to 1.0. If the SD is >1.2, it is likely that there was a lot of imprecision in measuring height and weight. Skewness WHZ Should be between -1 Measures the direction and degree to which the results are and +1 symmetrical.

Kurtosis WHZ Should be between -1 Measures the “peakedness” of the distribution. and +1

Poisson dist WHZ<-2 If p-value <0.05, this Examines the heterogeneity of the population. indicates that there are pockets of high Not a quality test. malnutrition.

Overall score WHZ Preferable below 25% A composite indicator of data quality.

The overall score WHZ is a composite score based on all indicators and must be below 25% (based on 16 November version of ENA for SMART, which may change as the software is developed). The plausibility report, based on the complete data set, must be included in the final report of a nutrition survey, with clear explanations where there are problems indicated by the report. 9. DATA ANALYSIS AND INTERPRETATION This section explains the steps for analyzing and presenting data as well as interpreting the findings of a nutrition survey.

9.1 Data analysis For anthropometry and mortality, results are automatically generated by ENA for SMART software.

Anthropometry Figure 26 shows the results for anthropometry in ENA for SMART.

Figure 26 Results anthropometry

After completing data entry, clicking the “Results Anthropometry” displays the results of the survey. 1. The main survey results are displayed with 95% Confidence interval (C.I) for moderate, severe and global malnutrition. The proportion of oedema cases is also displayed below the main results. The SD of WHZ is also displayed. As discussed in section 8, this is the key indicator of quality of anthropometric data. The final DEFF for the survey is also given. Additionally, the number of z-scores unavailable, as well as the z-scores out of range is also shown. The numbers depend on which flags have been set for exclusion of out of range z- scores. 2. The WHZ normal distribution is shown, with the WHO curve (in green) compared to the survey distribution (red). In this example, we can automatically see that the survey population has a higher prevalence of malnutrition than the reference population. 3. The results can be displayed either using the WHO 2006 or the NCHS 1977 references by clicking on either. 4. Results are displayed with 3 options of exclusion of z-scores: SMART flags, WHO flags, or no exclusion. 5. The results may be displayed for height-for-age, weight-for-age, or MUAC by changing the displayed options. 6. Results can also be displayed as follows: for all children, by sex, by cluster and by age category. 7. By clicking “Clipboard”, opening the Microsoft Word documents where you want to copy, and clicking paste/ctr-v, the WHZ curve displayed is copied to the Microsoft Word document.

Mortality Death rates are presented with 95% Confidence interval as well as the final DEFF. In the example shown (Figure 27), the CDR is 2.03 deaths per 10,000 per day (1.30-3.15, 95% C.I) with a design effect of 1.57.

Figure 27 Results mortality

Additional child indicators ENA SMART calculates the prevalence of malnutrition and disaggregates malnutrition by age, sex for both the NCHS and WHO for the three main indices (WFH, HFA, WFA). However, Vitamin A supplementation, measles vaccination and morbidity are not calculated in ENA for SMART, and different software must be used.

Measles vaccination and Vitamin A supplementation should be presented as shown in Table 9 to make a distinction between information collected from the mother/caregiver (recall) to information from the child health/vaccination card.

Table 9 Vitamin A supplementation and measles vaccination Measles Measles Vitamin A (with card) (with card or n= confirmation from mother) n= YES (No.) % (No.) % (No.) % (95% C.I.) (95% C.I.) (95% C.I.)

Note the age groups for Vitamin A (6-59 months) and measles (9-59 months).

Morbidity results should be presented in terms of: 1. The proportion of children reporting illness. 57

2. The prevalence of each illness (Table 10).

Table 10 Morbidity results 6-59 months Prevalence of reported illness % (95% C.I.) Diarrhoea Fever Cough Skin infections Eye infections

An additional question for iron-folate supplementation is asked to mothers or caregivers aged 15-49 years (Table 11).

Table 11 Antenatal care, Iron-folate Pregnant Attending Weight Pregnant women 15-49 antenatal care measured at women years least 4 times receiving iron during folate pregnancy (antenatal care) (No.) % (No.) % (No.) % (No.) % (95% C.I.) (95% C.I.) (95% C.I.) (95% C.I.)

IYCF The IYCF section analysis is summarised in Table 12 with reference to the questionnaire (Annex 6). Note that this module is for the 0-23 month age group, and that there are specific questions which look at sub-groups of the age range.

Table 12 IYCF analysis Indicator Questions Calculation for analysis Early initiation of Q5.4 Proportion of children 0-23.99 months who initiated breast feeding within breastfeeding one hour of birth (answer 1). Exclusive breastfeeding. Q5.5,5.8,5.9 Proportion of children 0-5.99 months who are currently breastfeeding (answer 1 to Q5.5), and not drinking any other liquids (answer 0 to Q5.8) and not eating any solid, semi-solid or soft foods (answer 0 to Q5.9). Continued breastfeeding Q5.5 Proportion of children 12-15.99 months currently breastfeeding (answer 1 at 1 year to Q6). Introduction of solid, Q5.9 Proportion of children 6-8.99 months eating solid, semi-solid or soft foods semi-solid or soft foods (answer 1 to Q5.9). Minimum dietary Q5.9 Proportion of children 6-23 months consuming at least 4 food groups diversity (answer 1 to at least 4 of the options in Q9). Minimum meal Q5.5,5.10 Proportion of non-breastfed children 6-23.99 months (answer 0 to Q5.5) frequency consuming solid, semi-solid or soft foods at least 4 times a day (answer >= 4in Q5.10). Minimum acceptable Q5.5,5.9,5.10 Proportion of children 6-23.99 months who are breastfeeding (answer 1 to diet (breastfeeding Q5.5), consuming at least 4 food groups (answer 1 to at least 4 of the children) options in Q5.9) and consuming solid, semi-solid or soft foods at least 4 times a day (answer >= 4in 5.10). Consumption of iron- Q5.11 Proportion of children 23.99 months who receive an iron-rich food or iron- rich or iron-fortified fortified food that is specially designed for infants and young children, or foods that is fortified in the home (answer 1 to Q5.11). Feeding during illness Q5.12 Frequencies for Q5.12 58

Note that due to the small sample size for age groups such as 0-5, 6-8 and 12-15, the estimates of prevalence are likely to have wide confidence intervals and results should be interpreted with caution.

Household-level indicators Guidance for data analysis for household-level indicators is given in Table 13.

Table 13 Household analysis: WASH and food security Indicator Questions Calculation Notes for analysis Main source of Q3.5 FS Calculate frequencies. household food Module Food Consumption Q3.6-3.14 To calculate FCS, multiply the See Figure 28 for weights used score (FCS) FS Module value obtained for each food and Figure 29 for categorisation. group by its weight (see food group weights in Figure 28) and create new weighted food group scores. Sum the weighed food group scores, thus creating the food consumption score (FCS).Note that frequency can only range from 0 to 7.

Proportion of HH using Q3.15 Calculate frequencies. fortified edible oils for food preparation Coping mechanisms Q3.16- The frequency of each coping 3.22 FS mechanism is calculated. Module Access to improved Q4.2 The variable is recoded to Types of water sources should be water source WASH “improved” and “unimproved” according to what is used in the Module local context, and therefore the options may change. An improved drinking-water source is defined as one that, by nature of its construction or through active intervention, is protected from outside contamination, in particular from contamination with faecal matter. Time for water Q4.3 Calculate frequencies. collection WASH Module Average number of Q4.4 Calculate average per household litres used per WASH household per day Module Treatment of water Q4.5 Run frequencies WASH Module Hand washing practices Q4.6 Calculate frequencies for each. WASH Proportion of caregivers Q4.7 Calculate % who wash hands/total For those who indicate that they washing hands with WASH washing hands with soap/ash. do wash their hands. water and soap or water and ash Access to improved Q4.8 The variable is recoded to “safe” Type of toilet facilities should sanitation WASH and “unsafe” sanitation. include only those used in that Module population, and therefore the 59

options may change. An improved sanitation facility is defined as one that hygienically separates human excreta from human contact. Definitions of improved water source and improved sanitation are from the Joint Monitoring Programme on Water supply and sanitation (WHO/UNICEF, 2013).

Figure 28 Food Consumption Score (FCS) weights. Source: WFP (2006)

An example of calculation is as follows: If a household consumed main staples 5 times during the past 7 days, the score for staples would be multiplied by the weight as follows: 5 x 2=10. The sum total of the group scores gives the FCS. Households are then categorized as shown in Figure 29 as poor, borderline, or acceptable. The thresholds have been defined by the food security cluster, taking into account the local diet. Adjustments have been made from the standard thresholds of: 0-21 poor, 21.5- 35 borderline, and >35 acceptable by taking into account the fact that, in Bangladesh nearly all households consume sugar and oil regularly.

Figure 29 Food Thresholds for FCS. Source: Bangladesh Food Security Cluster

9.2 Interpretation of results After analyzing results of a nutrition survey, the survey manager faces two key questions: 1. What does the level of malnutrition mean for this population? 2. What could explain the level of malnutrition?

Classification of severity of malnutrition It is important to be able to say whether the results of a nutrition survey can be called “normal” so as to indicate whether or not there is cause for concern, and therefore need for urgent attention.

WHO has established thresholds for severity of malnutrition based on population prevalence (Table 14).Therefore, if the prevalence of acute malnutrition exceeds 15% this is regarded as an emergency. It is however important to understand the levels of malnutrition which normally exist in the general population so as to make better judgement.

Table 14 Classification of severity of malnutrition (WHO) Severity of Prevalence of wasting (<- Prevalence of stunting Prevalence of underweight malnutrition 2 z-scores WFH) (<-2 z-scores HFA) (<-2 z-scores WFA) Acceptable <5% <20% <10% Poor 5-9% 20-29% 10-19% Serious 10-14% 30-39% 20-29% Critical >=15% >=40% >=30%

Classification of severity of mortality The most commonly used threshold for mortality is 1 per 10,000 per day for CDR and 2 per 10,000 children per day for 0-5 DR. High mortality normally indicates that there is a health problem, and is particularly worrying when malnutrition is high.

Comparing 2 surveys In addition to analyzing severity of malnutrition in the context of global thresholds, the results may be compared with previous surveys so as to be able to tell whether malnutrition has remained the same, decreased or increased. If there is a significant difference in malnutrition between 2 surveys, the 95% Confidence intervals will not overlap. For example, if one survey in 2012 found the prevalence of GAM to be 25.6% (22.9-28.5%, 95% C.I) and another survey on the same population in 2013 found the prevalence to be 23.0% (20.0%-26.2%, 95% C.I), there is no significant decrease in malnutrition (the 95% C.Is overlap).It is important to compare surveys conducted during the same period/season due to the seasonal variation shown by malnutrition, which may result in differences which are merely due to the season in which the survey was conducted.

9.2.4 Possible explanation for high malnutrition rates In interpreting results of nutrition surveys, the possible effect of potentially aggravating factors must be considered. These may include:  Poor household food availability  High market prices  Insecurity  Low levels of measles vaccination and Vitamin A supplementation.  Inadequate access to safe water and sanitation  Seasonal variations: Malnutrition shows seasonal variation, and therefore the results of a survey must be put in this context. In agricultural populations, there is normally a rise in malnutrition towards the end of the hunger season (lean period).If repeated surveys are to be conducted so as to monitor trends, it is advisable to collect data from around the same time each year.

 High mortality and and/or poor health indicators. Triangulation of data also helps us to understand that there is a problem with respect to malnutrition. Negative household coping mechanisms such as reduced meal frequency or frequent borrowing of food may indicate that the situation is severe. Monitoring dietary patterns may also show unusual consumption patterns which may explain high malnutrition rates.

10. REPORT WRITING AND DISSEMINATION This section outlines the format for reporting and explanation of each section of a standard survey format, as well as the dissemination of a completed survey report. The summary report follows the executive summary format of a standard SMART survey report automatically generated by ENA for SMART (Box 2). The summary report forms the main report.

10.1 Preliminary report

Box 2 Nutrition survey summary report format (one to two pages only)

· Geographic area surveyed and population type---brief description of the survey area and its population.

· Dates of survey: state dates for data collection.

· methodology used---explain sampling method, assumptions for sample size calculation, and main indicators).

· Main anthropometric results---(state prevalence of global and severe acute malnutrition, underweight, stunting in terms of z-scores and/or oedema and 95%

confidence intervals)

· Mortality rates (CMR and U5MR and 95% confidence intervals) CMR: (95% CI); U5MR: (95% CI)

· describe other important results (Vitamin A supplementation, Vaccination, IYCF etc).

· provide brief interpretation of the results

· give recommendations.

Due to the fact that nutrition survey data is normally analyzed within a relatively short period of time, the summary report can be disseminated within one week of the completion of data entry.

10.2 Final report The final report can normally be produced and disseminated within a month of completion of data entry/cleaning. The final report format follows the standard report which is automatically generated by ENA for SMART. The final report is more detailed than the preliminary report and includes all indicators defined in the analysis plan. The main sections of the report are as shown in Box 3:

Box 3 Nutrition survey final report format

Clicking in the ENA for SMART “Results Anthropometry” section generates a standard report format with the order of the text as well as automatically generated tables for anthropometry. The content follows the following format:

Executive summary (one to two pages only): description of geographic area,

population; dates of survey; sampling method and sample size calculation; main results for anthropometry and mortality with 95% C.I; interpretation of results with

recommendations.

1. Introduction: description of survey area; description of population; services and

humanitarian assistance; description of broad and specific objectives.

2. Methodology: justification of sampling method, explanation of sample size calculation, case definitions, inclusion criteria, training, data collection, data cleaning,

data analysis.

3. Results: description of survey results with tables and graphs.

4. Discussion: interpretation of results for anthropometry, mortality and other

indicators; possible causes, trends. 5. Conclusions: overall conclusion on severity of situation and response required.

6. Recommendations: immediate, medium term and long-term recommendations.

7. References: list of all secondary sources referred to in the text.

8. Acknowledgements: list of all government departments and other agencies who

participated, in all stages of the survey, donors who provided funding, as well as survey teams.

9. Appendices: Plausibility report, standardisation test results, assignment of clusters, calendar of events, referral form, questionnaires, cluster control form, maps of the

survey area.

11 RAPID NUTRITION ASSESSMENTS In a rapid-onset emergency, the priority is to obtain a snapshot of the nutrition situation as quickly as possible so as to verify the threat or existence of a nutrition emergency. Rapid nutrition assessments are therefore carried out in the initial stages of an emergency to establish if there is a nutrition problem and identify immediate needs. Rapid nutrition assessments differ from nutrition surveys in terms of methodology. Rapid nutrition assessments have limited use of quantitative data, which is restricted mainly to nutrition screening, and most of the data collected is qualitative. However, in nutrition surveys, the emphasis is on quantitative data collection methods. The steps for conducting a RNA generally follow the same as those for a nutrition survey.

11.1 Decide whether to conduct a RNA The need for a rapid nutrition assessment is often precipitated by an emergency, which may result in destruction of infrastructure, large-scale migration, breakdown of essential services, loss of property and social disruption, which may then reduce access to land and food, result in crowded settlements, and limit access to water, hygiene and sanitation, loss of earnings and separated families. Ultimately, this may result in malnutrition and disease (Box 4).

Box 4: Impact of an emergency on nutritional status

Box 4 Impact of emergency on population, household and individual nutritional status

Triggers Natural disaster Political/ (flood, drought, War economic earthquake) shock

Impact on population

Loss of Destruction of Large-scale Breakdown property Social infrastructure migration of essential

services and disruption business

Impact on households Lack of access Residence in Loss of to land, Lack of water, overcrowded earnings Families reduced sanitation, and access access to land settlements separated hygiene to health services

Impact on individuals Malnutrition Disease

Death

Source: (ENN, 2011), Nutrition in Emergencies

11.2 Define RNA objectives According to the Emergency Nutrition Network (ENN, 2011), rapid nutrition assessments (RNAs) are normally conducted to fulfil the following objectives:

 To verify the existence or threat of a nutrition emergency  To estimate the number of people affected  To establish immediate needs  To identify local resources available and external resources needed  To provide initial screening for inclusion in selective feeding programmes RNA helps to understand:  The drivers of the crisis and underlying factors  The scope of the crisis and humanitarian profile.  The status of populations living in affected areas  National capacities and response to the crisis.  Existing services and assistance for the population and existing gaps.  The strategic humanitarian priorities.

11.3 Define geographic area and population groups The geographic area for the RNA has to be clearly delimited (village, camps, settlements, urban slums, etc.) so as to clearly understand the level of representativeness of the results.

11.4 Meet local leadership and authorities Before conducting a RNA, it is important to notify the relevant local leadership and to obtain approval and clearance, including authorities responsible for security. Given that a RNA is often conducted as part of an inter-sector/inter-agency rapid assessment, it is important to inform those responsible for other sectors which may either participate in or have interest in the RNA.

11.5 Determine the timing of the RNA According to Inter Agency Standing Committee (IASC, 2008) guidance, RNAs should take place as soon as possible following the onset of an acute crisis (Figure 30). However, in cases of protracted emergencies which may become acute, and when access may be limited due to insecurity or weather, RNAs are also recommended. It is recommended, due to the urgency of the situation, that data collection and reporting should normally take only a few days (5-10 days maximum) Following the RNA, it may be decided that a more in-depth nutrition survey be conducted. A RNA can be conducted:  When nutrition information is required urgently.

 When resources for a full scale survey are limited.

 When accessibility and/or security limit access for a complete nutrition survey.

Figure 30 Timing of rapid assessments Source: (IASC, 2008, Initial Rapid Assessment Tool Guidance Notes

As stated in the Nutrition Cluster Contingency Planning document for Bangladesh, following a crisis, the nutrition cluster will participate in the Joint Needs Assessment (JNA) undertaken with other clusters and stakeholders in Phases I (to assess the scale of the disaster) and II (to further assess the impact of the disaster). The RNA therefore forms part of this JNA, and nutrition-relevant data should be collected at this stage. The nutrition cluster will then decide on whether to carry out a Phase III assessment, which is a SMART anthropometric nutrition survey. At this stage the findings may lead to long-term programming and monitoring activities and the Phase III assessment is therefore conducted to collect detailed information. These findings will be used for medium and long term intervention design and planning.

11.6 Gather population data and other data In order to make an appropriate decision on the sampling method to apply in the RNA, population data must be collected from the relevant authorities. Population data should be available up to the lowest unit of the population possible. In the case of a RNA in a district, upazila or union, the population data should be up to village level. Secondary data must also be collected on the status of nutrition, health, food security, water, and sanitation before the event or crisis which led to the RNA.

11.7 Select sampling method, determine sample size

Quantitative data It is advisable to select children using the “house-to-house” method, and to strictly adhere to this method so as to avoid selection bias. Gathering children a central location may result in some being missed out. For example, children who are sick or malnourished may be left at home, which may result in an under-estimation of malnutrition. The following sampling procedures are recommended depending on each of the following possible scenarios:

Scenario 1: Single settlement2 with 200 households or less Exhaustive sampling is recommended, with assessment teams visiting all households in the settlement and measuring all children.

2 Settlement may either be a block of houses, a village, or a camp

Scenario 2: Single settlement with more than 200 households Simple random sampling or systematic random sampling is recommended, with a sample of 200 children.

Scenario 3: More than 1 settlement A 25 clusters x 8 children (200 children) cluster design is recommended.

The number of households is calculated as follows:

Number of children under 5 per household =average household size X % children under 5=6 X 25%=1.50

Number of children 6-59 months (it is assumed that the 6-59 months group constitutes 90% of children under 5) =1.5 X 0.9=1.35

Number of households required =

=no of children 6-59 months required/no of children 6-59 months per HH

= 200/1.35=148.1 (rounded to 149)

Number of households per cluster =total number of households required/number of clusters =149/25=5.96 (rounded to 6)

The clusters in this case are selected using PPS by entering the population sizes of each settlement into ENA for SMART and assigning clusters as explained in section 4.5.2. As previously explained, the 6 households in each are selected using simple/systematic random sampling.

Qualitative data  5 key informant interviews within each settlement/cluster.  3 focus group discussions within each settlement/cluster. Additionally, baseline data will be obtained from survey reports, humanitarian bulletins, census reports and previous rapid assessment report.

11.8 Decide which data to collect The following data is collected: Quantitative data: MUAC screening data for children below 5 years and mothers. The questionnaire is shown in Annex 11.

Qualitative data Pre-crisis data:  Baselines for health and population statistics, livelihoods and access to services.  Status of health, nutrition, WASH, shelter, food security pre-crisis. 67

 Local capacities for emergency response as well as gaps.

In-crisis data:  Characterize the nature, scope and extent of the emergency;  Identify the most affected regions, populations and vulnerable groups;  Describe local capacities and gaps.  Identify priority needs.

11.9 Prepare supplies and equipment Supplies and equipment for RNA include:  MUAC tapes  Questionnaires/forms  Maps  Phones

11.10 Select and train assessment teams The number of assessment teams will depend on the sample size and the time required to complete the assessment. Each team may consist of 3 people, one of them being the supervisor, and the other 2 will conduct MUAC screening and administer the key informant interviews and focus group discussions. The RNA Manager will train the teams, and select team leaders and supervisors. Team members should be trained in MUAC screening as well as rapid appraisal methods. A supervisor should be assigned to each team so as to visit teams in the field, ensuring that households are selected properly, and ensuring the necessary equipment is available and that measurements are taken and recorded accurately. Unexpected problems nearly always arise during an assessment, and the supervisor is responsible to decide how to overcome them. The supervisor is also responsible for overseeing data entry, data analysis and report writing. It is particularly important to check cases of oedema.

11.11 Collect data and manage assessment teams Quantitative data collection MUAC screening is recommended for the quantitative part of the RNA to establish the rate of acute malnutrition. MUAC (with oedema identification) is recommended for use in the RNA rather than WHZ, due to its relative simplicity and the fact that MUAC is a higher predictor of mortality risk than WHZ. Within a short period of time, a high number of children can be easily screened with MUAC, which perfectly suits the conditions of a RNA. Caution must be taken in interpretation of results. The rate of acute malnutrition as measured by MUAC should not be compared to similar results obtained using WHZ as the two measure different children. MUAC tends to measure younger children as MUAC increases with age (CMAM Forum, 2012). Qualitative data collection Key informant interviews Individual interviews will be carried out to obtain sector-specific information from individuals selected due to their knowledge of the community and their sector leadership:  General population information. The Administrative government head is the recommended respondent. To provide information including: general information on the population, including population size and structure, livelihoods, incomes. General changes in population due to the crisis.  Nutrition and health information. The head of the health facility is the recommended respondent, to provide information including: priority health problems, disease outbreaks, vaccination rates, access of community to health services and adequacy, basis services and services for treatment of malnutrition.

 WASH. The government head of the responsible department is the recommended respondent, to provide information to enable an understanding of the pre- and in-crisis access to water and sanitation, and changes due to current crisis.  Food security. The head of the agriculture department in the government is the recommended respondent. To provide information to enable understanding of: condition of crops and livestock (current, pre-crisis), meal frequency, dietary diversity, negative coping mechanisms, and changes as a result of the current crisis. The data collection guide is in Annex 12.

Focus group discussions (FGD) Each group will be made up of 10-15 and will be homogenous in terms of sex. FGDs will be conducted in each village selected for the RNA. Three separate FGDs will be conducted for: community leaders, women and men. The form to be used for data collection is in Annex 13. The facilitator will introduce the discussion and promote an atmosphere of openness from a neutral position. Focus Group Discussions (FGD) are carried out to gather qualitative information reflecting community perception and perspective of:  The general outlook of the crisis?  Nutrition: feeding practices for infants and young children, and effect of crisis  Health: priority health problems, access to health services, and effect of current crisis.  WASH: population’s access to safe drinking water and sanitation, and possible changes as a result of the crisis.  Food security: food aid, condition of crops, livestock, food prices, meal frequency, negative coping mechanisms and possible effects of crisis.

Figure 29 Data entry in ENA for MUAC

11.12 Enter and clean data MUAC data is entered in ENA for SMART (Figure 29). Data cleaning should be based on checking questionnaires for missing values as well as running the plausibility check on a daily basis for MUAC data to analyze:  extreme values

 digit preference

 age ratio

 sex ratio

The same procedures described in section 8.2 should be used.

11.13 Analyze data and interpret findings Quantitative data MUAC and oedema data may be analyzed using ENA for SMART to estimate the proportion of children with moderate and severe acute malnutrition (Figure 31).

Figure 31 MUAC data analysis

11.13.2 Qualitative data Qualitative data should be grouped in terms of responses to summarize the main issues presented. Findings should be interpreted in terms of the prevalence of acute malnutrition and the background factors as identified by the key informant interviews and focus group discussions.

11.14 Write and disseminate report The results of RNA must normally be available within 3 to 5 days due to the urgency of response. The reporting format is shown in Box 5.

Box 5 Rapid Nutrition Assessment report format Introduction Background: what led to the RNA; who conducted the RNA and when? Describe secondary data on health, food security, WASH, livelihoods. Describe geographic area surveyed and population type: Objectives : what were the specific objectives of the RNA? Methodology : what sampling method was used and why? how was the sample size calculated; what variables were measured and what methods were used? how was data collected and analyzed? Summary of key findings Summary of the crisis • Overall judgment of humanitarian situation and the severity of needs identified • is the crisis is worsening or becoming less serious? • What are the underlying causes of problems and risks? • What are the threats to security (natural hazards, population movements, armed groups, etc)? • Are any population groups inaccessible, and why? • What are the risk-factors that could worsen humanitarian conditions or impede relief operations (bad weather, insecurity etc.)?

Problems and priorities identified by the affected population

Results of MUAC screening: children Name of Total screened Normal (>=125mm) Moderate (115- Severe (<115mm) settlement 124mm) No % (95% No % (95% No % (95% C.I) C.I) C.I)

Results of MUAC screening: adults Name of Total screened Normal (>=210mm) Moderate (185- Severe <185mm settlement 219mm) No % (95% No % (95% No % (95% C.I) C.I) C.I)

Annex 1 Weight-for-heiSectorght-specific z-score findings reference (WHO 2006) Sector Key observations Recommendations Population description Nutrition AnnexHealth 2 Local calendar of events AnnexFood security 2 Local calendar of events Shelter, non- Annexfood items 1 WHO reference table. WASH Weight-for-Length Look-up Table, Children 0-23 Months (Birth to 2 years), WHO 2006 Child Growth Standards 71

Boys' weight (kg) Length a Girls' weight (kg) -3 SD -2 SD -1 SD Median (cm) Median -1 SD -2 SD -3 SD 1.9 2.0 2.2 2.4 45 2.5 2.3 2.1 1.9

2.0 2.2 2.4 2.6 46 2.6 2.4 2.2 2.0

2.1 2.3 2.5 2.8 47 2.8 2.6 2.4 2.2

2.3 2.5 2.7 2.9 48 3.0 2.7 2.5 2.3

2.4 2.6 2.9 3.1 49 3.2 2.9 2.6 2.4

2.6 2.8 3.0 3.3 50 3.4 3.1 2.8 2.6

2.7 3.0 3.2 3.5 51 3.6 3.3 3.0 2.8

2.9 3.2 3.5 3.8 52 3.8 3.5 3.2 2.9

3.1 3.4 3.7 4.0 53 4.0 3.7 3.4 3.1

3.3 3.6 3.9 4.3 54 4.3 3.9 3.6 3.3

3.6 3.8 4.2 4.5 55 4.5 4.2 3.8 3.5

3.8 4.1 4.4 4.8 56 4.8 4.4 4.0 3.7

4.0 4.3 4.7 5.1 57 5.1 4.6 4.3 3.9

4.3 4.6 5.0 5.4 58 5.4 4.9 4.5 4.1

4.5 4.8 5.3 5.7 59 5.6 5.1 4.7 4.3

4.7 5.1 5.5 6.0 60 5.9 5.4 4.9 4.5

4.9 5.3 5.8 6.3 61 6.1 5.6 5.1 4.7

5.1 5.6 6.0 6.5 62 6.4 5.8 5.3 4.9

5.3 5.8 6.2 6.8 63 6.6 6.0 5.5 5.1

5.5 6.0 6.5 7.0 64 6.9 6.3 5.7 5.3

5.7 6.2 6.7 7.3 65 7.1 6.5 5.9 5.5

5.9 6.4 6.9 7.5 66 7.3 6.7 6.1 5.6

6.1 6.6 7.1 7.7 67 7.5 6.9 6.3 5.8

6.3 6.8 7.3 8.0 68 7.7 7.1 6.5 6.0

6.5 7.0 7.6 8.2 69 8.0 7.3 6.7 6.1

6.6 7.2 7.8 8.4 70 8.2 7.5 6.9 6.3

6.8 7.4 8.0 8.6 71 8.4 7.7 7.0 6.5

7.0 7.6 8.2 8.9 72 8.6 7.8 7.2 6.6

7.2 7.7 8.4 9.1 73 8.8 8.0 7.4 6.8

7.3 7.9 8.6 9.3 74 9.0 8.2 7.5 6.9

7.5 8.1 8.8 9.5 75 9.1 8.4 7.7 7.1

7.6 8.3 8.9 9.7 76 9.3 8.5 7.8 7.2

7.8 8.4 9.1 9.9 77 9.5 8.7 8.0 7.4

7.9 8.6 9.3 10.1 78 9.7 8.9 8.2 7.5

8.1 8.7 9.5 10.3 79 9.9 9.1 8.3 7.7

8.2 8.9 9.6 10.4 80 10.1 9.2 8.5 7.8

8.4 9.1 9.8 10.6 81 10.3 9.4 8.7 8.0

8.5 9.2 10.0 10.8 82 10.5 9.6 8.8 8.1

8.7 9.4 10.2 11.0 83 10.7 9.8 9.0 8.3

8.9 9.6 10.4 11.3 84 11.0 10.1 9.2 8.5

9.1 9.8 10.6 11.5 85 11.2 10.3 9.4 8.7

9.3 10.0 10.8 11.7 86 11.5 10.5 9.7 8.9 a Length is measured for children under 2 years or less than 87 cm height. For children 2 years or older or 87 cm height or greater, height is measured. Recumbent length is, on average, 0.7 cm greater than standing height; although the difference is of no importance to individual children, a correction may be made by subtracting 0.7 cm from all lengths above 86.9 cm if standing height cannot be measured.

Weight-for-Height Look-up Table, Children 24-59 Months, WHO 2006 Child Growth Standards Boys' weight (kg) Height a Girls' weight (kg)

-3 SD -2 SD -1 SD Median (cm) Median -1 SD -2 SD -3 SD

9.6 10.4 11.2 12.2 87 11.9 10.9 10.0 9.2

9.8 10.6 11.5 12.4 88 12.1 11.1 10.2 9.4

10.0 10.8 11.7 12.6 89 12.4 11.4 10.4 9.6

10.2 11.0 11.9 12.9 90 12.6 11.6 10.6 9.8

10.4 11.2 12.1 13.1 91 12.9 11.8 10.9 10.0

10.6 11.4 12.3 13.4 92 13.1 12.0 11.1 10.2

10.8 11.6 12.6 13.6 93 13.4 12.3 11.3 10.4

11.0 11.8 12.8 13.8 94 13.6 12.5 11.5 10.6

11.1 12.0 13.0 14.1 95 13.9 12.7 11.7 10.8

11.3 12.2 13.2 14.3 96 14.1 12.9 11.9 10.9

11.5 12.4 13.4 14.6 97 14.4 13.2 12.1 11.1

11.7 12.6 13.7 14.8 98 14.7 13.4 12.3 11.3

11.9 12.9 13.9 15.1 99 14.9 13.7 12.5 11.5

12.1 13.1 14.2 15.4 100 15.2 13.9 12.8 11.7

12.3 13.3 14.4 15.6 101 15.5 14.2 13.0 12.0

12.5 13.6 14.7 15.9 102 15.8 14.5 13.3 12.2

12.8 13.8 14.9 16.2 103 16.1 14.7 13.5 12.4

13.0 14.0 15.2 16.5 104 16.4 15.0 13.8 12.6

13.2 14.3 15.5 16.8 105 16.8 15.3 14.0 12.9

13.4 14.5 15.8 17.2 106 17.1 15.6 14.3 13.1

13.7 14.8 16.1 17.5 107 17.5 15.9 14.6 13.4

13.9 15.1 16.4 17.8 108 17.8 16.3 14.9 13.7

14.1 15.3 16.7 18.2 109 18.2 16.6 15.2 13.9

14.4 15.6 17.0 18.5 110 18.6 17.0 15.5 14.2

14.6 15.9 17.3 18.9 111 19.0 17.3 15.8 14.5

14.9 16.2 17.6 19.2 112 19.4 17.7 16.2 14.8

15.2 16.5 18.0 19.6 113 19.8 18.0 16.5 15.1

15.4 16.8 18.3 20.0 114 20.2 18.4 16.8 15.4

15.7 17.1 18.6 20.4 115 20.7 18.8 17.2 15.7

16.0 17.4 19.0 20.8 116 21.1 19.2 17.5 16.0

16.2 17.7 19.3 21.2 117 21.5 19.6 17.8 16.3

16.5 18.0 19.7 21.6 118 22.0 19.9 18.2 16.6

16.8 18.3 20.0 22.0 119 22.4 20.3 18.5 16.9

17.1 18.6 20.4 22.4 120 22.8 20.7 18.9 17.3 a Length is measured for children under 2 years or less than 87 cm height. For children 2 years or older or 87 cm height or more, height is measured. Recumbent length is, on average, 0.7 cm greater than standing height; although the difference is of no importance to individual children, a correction may be made by subtracting 0.7 cm from all lengths greater than 86.9 cm if standing height cannot be measured.

Annex 2 Local calendar of events

Season Holidays English months Bangla months Age (Months) Summer May day 1/05/14 May ‘14 Joustho 0 Summer 1st Boishakh 14/05/14 April ‘14 Boishakh 1 Summer Liberation day 26/03/14 March ‘14 Choutro 2 Winter Language day 21/02/14 Feb ‘14 Falgun 3 Winter Jan ‘14 Magh 4 Winter Victory day 16/12/13 Dec ‘13 Poush 5 Winter Nov‘13 Aghrahayon 6 Rainy Eid-ul-Azha Oct‘13 Kartik 7 Rainy Sep‘13 Ashin 8 Rainy Eid-ul-Fitre Aug‘13 Vadro 9 Rainy Ramadan July‘13 Srabon 10 Summer June‘13 Ashar 11 Summer May day 1/05/13 May ‘13 Joustho 12 Summer 1st Boishakh 14/05/13 April ‘13 Boishakh 13 Summer Liberation day 26/03/13 March ‘13 Choutro 14 Winter Language day 21/02/13 Feb ‘13 Falgun 15 Winter Jan ‘13 Magh 16 Winter Victory day 16/12/12 Dec ‘12 Poush 17 Winter Eid-ul-Azha Nov‘12 Aghrahayon 18 Rainy Oct‘12 Kartik 19 Rainy Eid-ul-Fitre Sep‘12 Ashin 20 Rainy Ramadan Aug‘12 Vadro 21 Rainy July‘12 Srabon 22 Summer June‘12 Ashar 23 Summer May day 1/05/12 May ‘12 Joustho 24 Summer 1st Boishakh 14/05/12 April ‘12 Boishakh 25 Summer Liberation day 26/03/12 March ‘12 Choutro 26 Winter Language day 21/02/12 Feb ‘12 Falgun 27 Winter Jan ‘12 Magh 28 Winter Victory day 16/12/11 Dec ‘11 Poush 29 Winter Eid-ul-Azha Nov‘11 Aghrahayon 30 Rainy Oct‘11 Kartik 31 Rainy Eid-ul-Fitre Sep‘11 Ashin 32 Rainy Aug‘11 Vadro 33 Rainy July‘11 Srabon 34 Summer June‘11 Ashar 35 Summer May day 1/05/11 May ‘11 Joustho 36 Summer 1st Boishakh 14/05/11 April ‘11 Boishakh 37 Summer Liberation day 26/03/11 March ‘11 Choutro 38 Winter Language day 21/02/11 Feb ‘11 Falgun 39 Winter Jan ‘11 Magh 40 Winter Victory day 16/12/10 Dec ‘10 Poush 41 Winter Eid-ul-Azha Nov‘10 Aghrahayon 42 Rainy Oct‘10 Kartik 43 Rainy Eid-ul-Fitre Sep‘10 Ashin 44 Rainy Aug‘10 Vadro 45 Rainy July‘10 Srabon 46 Summer June‘10 Ashar 47 Summer May day 1/05/10 May ‘10 Joustho 48 Summer 1st Boishakh 14/05/10 April ‘10 Boishakh 49 Summer Liberation day 26/03/10 March ‘10 Choutro 50 Winter Language day 21/02/10 Feb ‘10 Falgun 51 Winter Jan ‘10 Magh 52 Winter Victory day 16/12/09 + Eid-ul- Dec ‘09 Poush 53 Azha Winter Nov‘09 Aghrahayon 54 Rainy Eid-ul-Fitre Oct‘09 Kartik 55 Rainy Sep‘09 Ashin 56 Rainy Aug‘09 Vadro 57 Rainy July‘09 Srabon 58 Summer June‘09 Ashar 59

Annex 3 Referral form

REFERRAL FORM REFERRAL FORM

Date ------Date ------

Village: ------Village: ------

Name of child: ------Name of child: ------

Name of mother: ------Name of mother: ------

Sex: ------Sex: ------

Weight: ------Weight: ------

Height: ------Height: ------

MUAC ------MUAC ------

Oedema(y/n)------Oedema(y/n)------

Referred by ------Referred by ------

REFERRAL FORM REFERRAL FORM

Date ------Date ------

Village: ------Village: ------

Name of child: ------Name of child: ------

Name of mother: ------Name of mother: ------

Sex: ------Sex: ------

Weight: ------Weight: ------

Height: ------Height: ------

MUAC ------MUAC ------

Oedema(y/n)------Oedema(y/n)------

Referred by ------Referred by ------

Annex 4 Anthropometry module

Date______Division______District______Upazila______Union______Village______

Cluster___ HH Number__ Team Number__ HH ______Child Number__

1.2 Sex (m=Male, f=Female)

1.3 Age in months or

Date of birth (DD/MM/YYYY)

1.4 Weight (+/- 0.1kg)

1.5 Height (+/- 0.1cm)

1.6 Oedema (y=YES, n=NO)

1.7 MUAC (mm)

1.8 Has the child received Vitamin A supplementation in the last 6 months?

0=No, 1=YES

1.9 Has the child received measles vaccination?

1=YES (card), 2=YES (mother), 3=NO, 9=Don’t know

1.10 Did the child suffer from any of the following?

1=Diarrhoea

2=Fever 78

3=Cough

4=Skin infections

5=Eye infections

1.11 If diarrhoea, did the child consume ORS and (10) Zinc tablets? 1=YES, 0=NO

1.12 Are you pregnant 0=No, 1=Yes

1.13 Are you attending antenatal care? 1=YES, 0=NO

1.14 (If attending ANC), has your weight been measured at least 4 times during pregnancy? 1=YES, 0=NO

1.15 Are you receiving iron-folate? 0=No, 1=Yes

1.16 Have you increased your diet intake as a result of your pregnancy? 1=Yes, 0=No

Annex 5 Mortality Module

Date______Division______District______Upazila______Union______Village______

Cluster___ HH Number__ Team Number__ HH ______

ID 1 2 3 4 5 6 7 HH member Present Present at Sex Age in Born Died during recall now beginning years during period of recall period recall (include those period not present now)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Tally (Data entered in ENA for SMART for each household)

Current HH members (total) Current HH members (below 5 years) Current HH members who arrived during recall (exclude births) Current HH members who arrived during recall (below 5 years) Past HH members who left during recall (exclude deaths) Past HH members who left during recall (below 5 years) Births during recall Deaths during recall (total) Deaths during recall (below 5 years)

Annex 6 IYCF Module

Date______Division______District______Upazila______Union______Village______

Cluster___ HH Number__ Team Number__ HH ______Child Number__

5.2 Age in months or Date of birth (DD/MM/YYYY)

5.3 Has this child ever been breastfed? 0=No, 1=Yes

5.4 How long after birth did you put your child to the breast?

1= Within 1 hour 2=In the first day 3=After the first day

5.5 Was this child breastfed yesterday during the day or night?

(1=YES, 0=NO) 5.6 Was (Name) given any vitamin drops or other medicines as drops yesterday during the day and night?. 0 = No 1 = Yes 5.7 Was (Name) given ORS yesterday during the day and night? 0 = No 1 = Yes

5.8 What liquids was this child given yesterday during the day or at night? (multiple response)

1 = plain water 2 = infant formula (add local brand) 3 = milk (tinned, powdered, or fresh animal milk) 4 = juice or juice drinks 5 = clear broth 6 = other water based liquids 7 = sour milk or yoghurt 8 = thin porridge 9 = any other liquid

5.9 What foods were given to the child yesterday during the day and night? (multiple response) 1 = Rice, noodles, bread, roots and tubers 2 = Legumes and nuts 3 = Dairy products (milk, yogurt, cheese, sour milk) 4 = Flesh foods (meat, fish, poultry and liver/organ meats) 5 = Eggs 6 = Vitamin A rich fruits and vegetables (Ripe mangoes, ripe pawpaw, dark green vegetables) 7 = Other fruits and vegetables. 8 =Any oil, fats or butter, or foods made with any of these Any dark green leafy vegetables?

5.10 How many times did the child eat solid and, semi solid or soft foods other than liquids yesterday during day or at night? (Insert number)

5.11 Yesterday during the day or night, did the child eat any of the following foods. 0=No 1= Yes 1= Iron fortified foods (Insert local foods) 2=Micronutrient powders/sprinkles 3=Lipd based nutrient supplements (eg RUTFs) 81

5.12 How much food/drink do you give this child to eat/drink when having diarrhoea compared to when s/he is healthy? ______0= Nothing at all 1= Fluids only 2= Less fluids than usual 3= More fluids than usual 4= Less food than usual 5= More food as usual 6= ORS 7= Less breastfeeding 8 = More breastfeeding ______Do not prompt. More than 1 answer possible

Annex 7 Food Security Module

Date______Division______District______Upazila______Union______Village______

Cluster___ HH Number__ Team Number__ HH ______

3.2 How many people live in this household?

3.3 Resident status of HH 1=Resident 2=Returness (in the past year) 3=IDP 4=Refugee 66=Other (specify)

3.4 Is the head male of female? 1=Male 2=Female

3.5 What was the main source of food for this household? ------1 = Own production 2 = Work for food 3 = Gifts 4 = Market/ shop purchase 5=Borrowing/ debt 6 = Food aid 7 = Hunting 8 = Fishing 9 = Gathering 10 = Other

3.6 to I would like to ask you about the different foods your household members have eaten in the last 7 days. 3.14 Could you please tell me how many days in the last 7 days your household member (s) have eaten the following foods? If different members ate a different number of days, then consider the highest number of days.

Starchy foods (Rice, wheat, muri, potatoes, sweet potatoes, maize, kichuri)

Vegetables

Meat (Beef, Goat and Chicken), Eggs and Fish

Fruits

Pulses (any type of dal)

Milk, yogurt and other dairy

Oil, fat, butter

Sugar, honey

Condiments/other (Tea, Coffee, spices, etc.

3.15 Do you use fortified edible oil in preparing meals for young children? 0=No, 1=Yes

3.16- In the past 30 days, have any members of this household done any of the following? (1=YES, 0=NO) 3.22 1. Skip meals 5. Reduce adult food intake to allow children to eat

2. Reduce the size of meals 6. Send children to eat with relatives

3. Eat less preferred foods (e.g. wild foods etc.) 7. Sell productive assets 83

4. Borrow (food/money to purchase food) from relatives

Annex 8 WASH Module

Date______Division______District______Upazila ______Union______Village______

Cluster___ HH Number__ Team Number__ HH ______

4.2 What is the household’s main source of drinking water?

1 = Borehole 2 = Protected shallow well 3 = Open shallow well 4 = Protected spring 5 = River /stream 6 = HH connection/ stand pipe/ tanker 7 = Dam/ pond 66 = Other (specify)

How long does it take to collect HH water (including travel to and from and waiting)? ------4.3 1 = <30 min 2 = >30min to <1hr 3 = >1hr to < 2hr 4 = >2hr to < 4hr 5 = >4hr 4.4 How many litres (jerry cans) of water did the HH use yesterday in total (excluding water for washing clothes)? (define how many litres a jerry can hold if the population all use the same. Consider balance of the fetched amount & left over) 4.5 What is done to the water before household members drink it? ------0 = Nothing 1 = Boiling 2 = Filtering with a cloth 3 = Letting it settle 4 = Chlorination 66 = Other (Specify)

4.6 When do you usually wash your hands during the day (list all options mentioned)? ------0 = Never 1 = After defecating 2 = After cleaning child feces 3 = Before cooking 4 = Before eating 5 = Before breast feeding 66 = Other (specify) 4.7 What do you use to wash hands? ------0= Nothing 1= Water only 2= Water + soap 3= Water + ash 66= Other (specify) 4.8 What type of toilet facility do you use? ------1 = Flush toilet 2= Piped sewer system 3=Septic tank 4=Pour/flush to pit latrine 85

5=Ventilated improved pit latrine 6= Pit latrine with slab 7= Composting toilet 8= Flush/por to elsewhere 9= Pit latrine without slab 10= Bucket 11 = Hanging toilet/latrine 12 = No facility/bush

Annex 10 Questionnaires in Bengali অ্যান闍রা闍 া闍েট্রি এণ্ড েযাটারনাল েডিউল Anthropometry and Maternal Module তাডরখ (ডিন/োস/সাল): ...... /...... /...... ক্লাস্টার নং: ...... 綿ে নং: ...... ডরডিওন: ...... জিলা: ...... গ্রাে: ...... ১.১ ১.২ ১.৩ ১.৪ ১.৫ ১.৬ ১.৭ ১.৮ ১.৯ ১.১০ ১.১১ ১.১২ ১.১৩ ১.১৪ ১.১৫ ১.১৬ অ্সুতার ধরণ (গত ২ সপ্তা闍) (িায়ডরয়া (যা闍ল) 1=িায়ডরয়া গিড বতী 闍য় প্রসব ূবডবতী গিডোলীন আয়রণ- বার ি闍ল 2=জ্বর-সডিড থাে闍ল) গিডবতী? জসবা গ্রণ সে闍য় ি闍লট আ ডন ডে বয়স (ো闍স) া闍ের 綿ো 3= জ্বর-সডিড ও ডলঙ্গ ডিটাডেন এ (গত েয় বাচ্চা ডে ০=না ের闍েন? েে 闍ে টযাব闍লট আ নার অ্থবা ওিন উচ্চতা ইডিো েুয়াে (ডেডে闍ত) ০=না শ্বাসেষ্ট খানা m=জেইল ো闍স) ওরসযালাইন 1=যা ০=না চার বার গ্রণ খাবা闍রর িন্ম তাডরখ (জেডি闍ত) (জসডে闍ত) n= নাই (জেেন: 1=যা (োিড ) 4=চােড়ায় নং f=ডি闍েই ০=না এবং ডিঙ্ক না 闍ল 1=যা ওিন ো া ের闍েন? ডরোণ (ডিন/োস/সাল) (জেেন: ১২.৪) (জেেন: ৭৮.১) y=আ闍ে ১১৩.০) 2= যা (ো সংক্রেণ ল 1=যা টযাব闍লট এখা闍নই না闍ল 闍য়ডেল ০=না বাডড়闍য়闍েন ব闍ল闍ে) 5=জচা闍খ জখ闍য়ডেল? থােুন এখা闍নই ডে? 1=যা ? সংক্রেণ ০=না ০=না থােুন ০=না (এ闍ের অ্ডধে 1=যা 1=যা 1=যা উত্তর 闍ত া闍র)

েটডাডল綿 েডিউল Mortality Module

তাডরখ (ডিন/োস/সাল): ...... /...... /...... ক্লাস্টার নং: ...... 綿ে নং: ...... জিলা: ...... গ্রাে: ...... খানা নং: ...... ২.১ ২.২ ২.৩ ২.৪ ২.৫ ২.৬ ২.৭ ২.৮ আই ডি খানা সিসয闍ির নাে বতডো闍ন উ ডত (যা 闍ল ডরে闍লর শু쇁闍ত উ ডত (যা বয়স ( ূণড বে闍র) ডলঙ্গ ডরে闍লর সেয় িন্ম闍য়ডেল(যা ডরে闍লর সেয় েৃতু য  綿ে ডিন, না 闍ল× ডিন) 闍ল  綿ে ডিন, না 闍ল  綿ে ডিন, না 闍য়ডেল(যা 闍ল  綿ে 闍ল×ডিন) 闍ল×ডিন) ডিন, না 闍ল × ডিন)

বতডোন খানা সি闍সযর সংখযা বতডোন খানা সি闍সযর সংখযা বতডোন খানা সি闍সযর সংখযা বতডোন খানা সি闍সযর সংখযা ডরে闍লর সেয় চ闍ল োওয়া ডরে闍লর সেয় চ闍ল োওয়া িন্ম সংখযা েৃতু যসংখযা েৃতু য (৫ বে闍রর নী闍চ) (জোট) (৫ বে闍রর নী闍চ) োরা ডরে闍লর সেয় এ闍স闍ে োরা ডরে闍লর সেয় এ闍স闍ে অ্তীত খানা সি闍সযর সংখযা অ্তীত খানা সি闍সযর (৫ বে闍রর নী闍চ) সংখযা(৫ বে闍রর নী闍চ)

িুি ডসডেউডর綿 েডিউল Food Security তাডরখ (ডিন/োস/সাল): ...... /...... /...... ক্লাস্টার নং: ...... 綿ে নং: ...... ডরডিওন: ...... জিলা: ...... গ্রাে: ...... ৩.১ ৩.২ ৩.৩ ৩.৪ ৩.৫ ৩.৬ ৩.৭ ৩.৮ ৩.৯ ৩.১০ ৩.১১ ৩.১২ ৩.১৩ ৩.১৪ ৩.১৫ ৩.১৬ ৩.১৭ ৩.১৮ ৩.১৯ ৩.২০ ৩.২১ ৩.২২ খানা খানার খানার আবাডসে খানা প্রধান খানার খাবা闍রর আডে আ না闍ে ডবডি খাবার, ো আ নার খানার সিসযরা গত সাত ডি闍নর ে闍ধয জখ闍য়闍ে, সম্প闍েড ডিজ্ঞাসা জোট গত ডিশ ডি闍ন খািযািা闍বর োর闍ণ আ নার খানার জেউ ডে নী闍চর বযা ার巁闍লার সুখীন নং সিসয অ্বা ১=জেইল প্রধান উৎস েী? েরব। আ নার খানার সিসযরা গত সাত ডি闍নর ে闍ধয েত ডিন ঐ খাবার巁闍লা জখ闍য়闍ে তা বল闍বন। েডি ডি বাচ্চার 闍য়闍েন? সংখযা 1=ায়ী ২=ডি闍েইল 1=ডনিস্ব ডি সিসয ডি ডি সংখযে ডিন জখ闍য় থা闍ে তা闍ল স闍বডাচ্চ সংখযে ডিন綿 ডব闍বচনা ে쇁ন। খাবার ০=না বসবাসোরী উৎ ািন প্রস্তুডত闍ত 1=যা আ ডন 2=প্রতযাবতী 2=ো闍ির জশ্বতসার শােসবডি োংস িল েলাই 駁ধ, িই জতল, ডচডন, েসলা/অ্নযানয জোন জোন অ্闍 োেৃত আত্নীয়- বাচ্চা闍ির বাচ্চা闍ির উৎ ািনশীল ডে 3=অ্িযন্তরীণিা闍ব ডবডনে闍য় খািয িাতীয় (গ쇁, (িাল এবং চডবড, েধু (চা, েডি,) জবলা জবলা েে স্বি闍নর খাওয়া闍নার খাওয়া闍নার সম্পি ডবডক্র সেৃদ্ধেৃত ডনবডাডসত 3=উ ার খািয োগল িাতীয় অ্নযানয োখন না েে ে闍ের োে িনয িনয ে闍রডেল খাবার 4=শরণাথী 4=বািার/জিাোন (িাত, এবং জে 駁গ্ধিাত জখ闍য় জখ闍য় খাবার জখ闍য় জথ闍ে বয়স্করা আত্নীয়- জতল 66=অ্নযানয জথ闍ে ক্রয় গে, েুরডগ), জোন খাবার ডেল ডেল ডেল খাবার বা েে স্বি闍নর বযবার (ডনডিডষ্ট ে쇁ন) 5=ধার/ঋণ েুডড়, ডিে শসয) খাবার জখ闍য়ডেল ো闍ে ে闍রন? 6=খািয সাােয আলু, এবং জেনার া膿闍য় ০=না 7=ডশোর ডেডষ্ট োে টাো ডি闍য়ডেল 1=যা ধার 8=েৎসযডশোর আলু, ে闍রডেল 9=খাবার িু ট্রা, জটাো闍না ডখচু ডড়) 10=অ্নযানয

ওয়াটার এণ্ড সযাডন闍টশন েডিউল Water and Sanitation Module (জেসব খানার অ্যান闍রা闍 া闍েট্রি এণ্ড জলথ,িুি ডসডেউডর綿 এণ্ড লাইিডলহুি এবং আই.ওআই.ডস.এি.সম্পডডেত তথয সংগৃীত 闍য়闍ে জসখা闍ন প্রশ্ন ে쇁ন) তাডরখ (ডিন/োস/সাল): ...... /...... /...... ক্লাস্টার নং: ...... 綿ে নং: ...... ডরডিওন: ...... জিলা: ...... গ্রাে: ...... ৪.১ ৪.২ ৪.৩ ৪.৪ ৪.৫ ৪.৬ ৪.৭ ৪.৮ খানা নং খানার খাবার াডনর এবং রাা-বাার খানার াডন জোগাড় ের闍ত গতোল খানায় সবড闍োট েত খানার সিসযরা ান েরার আ闍গ ডি闍ন সচরাচর েখন আ ডন াত জধায়ার িনয আ ডন েী বযবার েী ধর闍ণর জশৌচাগার সুডবধা িনয বযবহৃত াডনর প্রধান উৎস েতেণ লা闍গ (োওয়া-আসা এবং ডলটার াডন বযবার েরা াডন闍ে েী ে闍রন? (োি এে綿 আ নার াত জধৌত ে闍রন? ে闍রন? (োি এে綿 উত্তর) আ নারা বযবার ে闍রন? (োি জোন綿? অ্闍 োস)? 闍য়闍ে? ( া闍ির ডলটার সংখযার উত্তর) (সব巁闍লা উত্তর ডলখুন) 0=ডেেু না এে綿 উত্তর) 1= ো綿闍ত খুুঁড়া গিীর গতড 1=<৩০ ডেডনট সা闍থ েতবার াডন আনা 闍য়闍ে 0=ডেেু না 0=েখ闍না না 1=শুধু াডন 1=ফ্ল্যাশ টয়闍লট 2=綿উবও闍য়ল 2=>৩০ ডেডনট-<১ ঘণ্টা তা 巁ণ ে闍র ডসাব ে쇁ন) 1=িুটা闍না 1= ায়খানার 闍র 2= াডন+সাবান 2= াই েুক্ত য়:ডনষ্কাশন বযবা 3= জখালা অ্গিীর 嗁য়া 3=>১ ঘণ্টা-<২ ঘণ্টা 2=ো ড় ডি闍য় োুঁো闍না 2=বাচ্চার ায়খানা ডরষ্কার েরার 3= াডন+োই 3=েল闍শাধনী 4=ডনরা ি ঝণডা 4=>২ ঘণ্টা-<৪ ঘণ্টা 3=ডথতা闍না 闍র 66= অ্নযানয (ডনডিডষ্ট ে쇁ন) 4= াডন ডি闍য়/ফ্ল্াশ ে闍র ো綿র 5=নিী/িলপ্রবা 5=>৪ ঘণ্টা 4=জক্লাডর闍নশন 3=রাা েরার আ闍গ গ闍তড 5= াডন ডি闍য়/ফ্ল্াশ ে闍র 6=খানার াডন-সং闍োগ/স্টযাণ্ড 66=অ্নযানয (ডনডিডষ্ট ে쇁ন) 4=খাওয়ার আ闍গ অ্নযি াই /টযাংোর 5=নয ান েরা闍নার আ闍গ 6=বায়ু চলাচ闍লর সুডবধােুক্ত উত 7=িলবন/ ু嗁র 66= অ্নযানয (ডনডিডষ্ট ে쇁ন) ড ট জলট্রিন 66=অ্নযানয (ডনডিডষ্ট ে쇁ন) 7=স্ল্যাবেুক্ত ড ট জলট্রিন 8=ে闍ম্পাডস্টং টয়闍লট 9= স্ল্যাবডবীন ড ট জলট্রিন 10= বালডত 11=ঝুলন্ত টয়闍লট 12= জোন জঝা ঝাড়/সুডবধা জনই

ইনিযাণ্ট এণ্ড ইয়াং চাইল্ড ডিডিং েডিউল Infant and Young Child Feeding Module (০-২৩.৯ োস বয়সী বাচ্চা আ闍ে এ রেে প্র闍তযে綿 খানায় ২ বে闍রর েে বয়সী প্রডত綿 বাচ্চার িনয িডর ডরচালনা ের闍ত 闍ব, ডেন্তু ২৪-৫৯ োস বয়সী বাচ্চার িনয ের闍ত 闍ব না) তাডরখ (ডিন/োস/সাল): ...... /...... /...... ক্লাস্টার নং: ...... 綿ে নং: ...... ডরডিওন: ...... জিলা: ...... গ্রাে: ...... ৫.১ ৫.২ ৫.৩ ৫.৪ ৫.৫ ৫.৬ ৫.৭ ৫.৮ ৫.৯ ৫.১০ ৫.১১ ৫.১২ খানা জরিা闍রন্স নং বয়স (ো闍স) অ্থবা এই বাচ্চা闍ে ডে প্রথে বাচ্চার েুখ (বাচ্চার নাে) জে (বাচ্চার নাে) জে (বাচ্চার নাে) জে (বাচ্চার নাে) জে (বাচ্চার নাে) জে (বাচ্চার নাে) (বাচ্চার নাে) ডে গতোল (বাচ্চার নাে) এর েখন াতলা িন্ম তাডরখ েখ闍না নয ান 闍ন জর闍খডে闍লন ডে গতোল ডি闍নর ডে গতোল ডি闍নর ডে গতোল ডি闍নর গতোল ডি闍নর জবলা গতোল ডি闍নর জবলা গতোল ডি闍নর ডি闍নর জবলা অ্থবা রা闍ত ায়খানা য় তখন তা闍ে তার সু (ডিন/োস/সাল) েরা闍না 闍য়闍ে? ি闍ন্মর েতেণ জবলা অ্থবা রা闍ত জবলা অ্থবা রা闍ত জবলা অ্থবা রা闍ত এবং রা闍ত তরল িাতীয় এবং রা闍ত েী েী জবলা এবং রা闍ত নী闍চর জোন খাবার綿 অ্বার তু লনায় জেেন ডরোণ ০=না 闍র? নয ান েরা闍না জোন ডিটাডেন সযালাইন খাওয়া闍না েী েী খাওয়া闍না খাবার খাওয়া闍না িারী (শক্ত), জখ闍য়ডেল? খাবার/ ানীয় জখ闍ত/ ান ের闍ত 1=যা 1= এে ঘণ্টার ে闍ধয 闍য়ডেল? অ্থবা অ্নয জোন 闍য়ডেল? 闍য়ডেল? 闍য়ডেল? অ্ধডশক্ত এবং তরল জলৌ সেৃদ্ধেৃত খাবার/ জিন? 2= প্রথে ডি闍নর ০=না ঔষ闍ধর জিাুঁটা ০=না 1= সাধারণ াডন 1= িাত, নুিলস, িাতীয় খাবার োড়া অ্ণু ুডষ্ট 巁ুঁড়া/ 0= ডেেুই না ে闍ধয 1=যা জিওয়া 闍য়ডেল? 1=যা 2= বািারিাতেৃত 쇁綿, েূল এবং েে অ্নযানয নরে খাবার ডলড ি-জবিি ডনউট্রি闍য়ন্ট 1=শুধুোি ানীয় েত বার 3= প্রথে ডিন 闍র ০=না ডশশু-খািয (জনস闍ল, 2= িাল, শুুঁ綿, বািাে সাডি闍েন্ট (জেেন, 2= স্বািাডব闍ের তু লনায় েে জখ闍য়ডেল 1=যা িা闍না, ডন闍িা, 3= 駁গ্ধিাত খাবার ? আর.ইউ.綿.এি.) ানীয় (সংখযা বসান) জস闍রলাে, প্রাইো) (駁ধ, িই, ডনর, টে ০=না 3= স্বািাডব闍ের তু লনায় জবডশ

3= 駁ধ (জেৌটািাত, িই) 1=যা ানীয় 巁ুঁ闍ড়া, অ্থবা প্রাণীর 4= োংসল খাবার 4= স্বািাডব闍ের তু লনায় েে তািা 駁ধ) (োংস, োে, াুঁস- খাবার 4= ি闍লর রস/িুস েুরডগ, েডলিা/অ্গডান 5= স্বািাডব闍ের তু লনায় জবডশ 5= াতলা সুয ডেটস) খাবার 6= অ্নযানয াডনিাত 5= ডিে 6= ওরসযালাইন তরল 6= ডিটাডেন এ সেৃদ্ধ 7=স্বািাডব闍ের তু লনায় েে বু闍ের 7= টে 駁ধ অ্থবা িই িলেূল এবং 駁ধ 8= াতলা সুডি শােসবডি ( াো 8= স্বািাডব闍ের তু লনায় জবডশ বু闍ের 駁ধ 9= অ্নয জে জোন আে, াো জ ুঁ闍, তরল গাঢ় সবুি শােসবডি) তাড়াহুড়া ের闍বন না। এ闍ের 7= অ্নযানয িলেূল অ্ডধে উত্তর সব এবং শােসবডি

Annex 10 Cluster control form

Date: _____ / _____ / ______District: ______Village: ______Cluster _____Team ______

Visit Result 1 = No of completed eligible HH needs Household Number of HH Head of HH 2 = part children to be revisited eligible Comments no name completed measured revisited children YES/NO 3 = refused YES/NO 4 = family not found 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Annex 11 MUAC screening form

Date ______Region ______District ______

Cluster ______Team ______

HH Child Sex Age in MUAC (mm) Oedema (Y/N) Classification No No months (Normal/ Moderate/ Severe) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Summary

Total screened Normal Moderate Severe

Comments: ______

______

Annex 12 Key informant interview guidance sheet

Question Population description What is the estimated population of this area and sources? What is the community’s main livelihood?

What is your perception of the current situation? (causes, effects, severity, assistance required)

Are there any unusual deaths due to the current crisis?

If yes, can you estimate the numbers of death? Which age group is the most affected?

Is there any unusual migration due to the recent crisis? If yes, where have they migrated to? What is your perception of the current situation? (causes, effects, severity, assistance required) Nutrition What is your perception of the current situation? (causes, effects, severity, assistance required) What are the rates of acute malnutrition in this community?

Are there any recent changes in malnutrition rates/admissions for malnutrition? What services are available for treating malnourished children?

What nutrition interventions are currently being implemented in this area? What are the main infant and young child feeding practices for this community? Health What is your perception of the current situation? (causes, effects, severity, assistance required) What are the main health problems in this community? Have there been any recent disease outbreaks? What are the rates of infant mortality: infant, crude, maternal Are there any changes in health problems as a result of this crisis? What are the basic health services provided by this health facility? Are the health services accessible within a reasonable distance and able to support this community sufficient? What gaps exist? What are the rates of measles vaccination for children? Food security What is your perception of the current situation? (causes, effects, severity, assistance required) What are the main income sources/livelihoods for households in this population? Is the staple food available on the market? What are the prices? Are there any changes as a result of this crisis? What negative coping mechanisms are practiced by this community? What is the condition of crops and livestock in this community? Are there any changes as a result of this crisis? What are the current food aid programmes for this community and how many are benefitting? What are the food security interventions currently being implemented in this area? WASH What is your perception of the current situation? (causes, effects, severity, assistance required) What proportion of households has access to safe drinking water? Is drinking water sufficient? What are the main sources of drinking water? Are there any changes in WASH due to the crisis? What are the main drinking water sources in this community? Annex 13 Focus groupIs drinking discussion water sufficient? guidance sheet What are the main types of95 toilet used by households in this community?

General  What is your perception of the crisis (the cause and the outlook)?  How is life in the community?  Discuss the types of problems currently faced?  Outline the pre-emergency conditions in the affected area (for example determine when the last good

Annex 14 Glossary of terms

Anthropometry - The study and technique of taking body measurements, especially for use on a comparison or classification basis. Bias - A consistent, repeated difference of the sample from the population, in the same direction; sample values that do not centre on the population values but are always off in one direction. Cluster sample - The selection of groups that are geographically close to one another for a sample; usually used in instances when lists of households or individuals are not readily available. Confidence interval - An interval that has a specified probability of covering the true population value of a variable or condition. Design effect - The loss of sampling efficiency resulting from the use of cluster sampling instead of random sampling. Food Consumption Score - a composite score based on dietary diversity, food frequency, and relative nutritional importance. Household – A group of people who normally live together and eat from the same pot. Index - An index is usually made up of two or more unrelated variables that are used together to measure an underlying characteristic. Indicator - A measure used at the population level to describe the proportion of a group below a cut- off point. Length-for-age - An index of past or chronic nutritional status; an index which assesses the prevalence of stunting. Local events calendar - A calendar that reflects important local events and seasons that might help a parent pinpoint the birth date of their child. Mean - The average value for a set of data; a measure of central location obtained by adding all the data items and dividing by the number of items. Median - A measure of central location for a set of data; the value that falls in the middle of a set of data when all the values are ordered from lowest to highest. Morbidity - A condition resulting from or pertaining to disease; illness. Mortality rate - Death rate; frequency of number of deaths in proportion to a population in a given period of time; death. NCHS reference standards – Growth percentiles developed by the National Centre for Health Statistics in the US that provide standards for weight-for-age, length-for-age and weight-for-length. Normal distribution - A normal distribution takes a bell-shape and has the following characteristics: the highest point occurs at the mean; it is symmetric; the standard deviation determines the width of the distribution; and it can be described with only two numbers: the mean and the standard deviation. Nutritional surveillance - A system of data collection and application; systems that are based on routinely compiled data and that monitor changes in variables over time, give warning of impending crisis or monitor the effectiveness/ineffectiveness of existing programs and policies; the continuous monitoring of the nutritional status of a specific group. Oedema - The presence of excessive amounts of fluid in the intercellular tissue. It is the key clinical sign of a severe form of protein energy malnutrition. Percentiles - A number that corresponds to one of 100 equal divisions in a range of values; a measure of relative location. For example, the 60th percentile means that 60% of values in the data set are less than or equal to it and (100 - 60) 40% are greater than or equal to it. Percentage of the median - A fraction or ratio based on a total of 100, where the median value of the data set equals 100; a value that equals a proportion or part of a distribution where the median represents 100 percent. Population - The entire group of people that is the focus of the study (everyone in the country, or those in a particular location, or a special ethnic, economic or age group). Prevalence - The proportion of the population that has a condition of interest (i.e. wasting) at a specific point in time; a measure of a condition that is independent of the size of the population; a value that is always between 0 and 1. References or reference standards -Measurement data collected on representative, healthy populations through standardized methods; a data set that allows comparisons to be made between its values and individuals or populations being measured.

Sample - A part or subset of the population used to supply information about the whole population. Sample size - The number of households or persons selected to be included in a sample or survey. Sampling - The technique of selecting a representative part of the population for the purpose of determining characteristics of the whole population. Sampling error - The difference between the results obtained from a survey sample and those that would have been obtained if the entire population was surveyed. The size of sampling error varies both with the size of the sample and with the percentages giving a particular response. Screening - The practice of distinguishing between individuals who should be enrolled in a program/intervention and those who should not be enrolled; a tool for identifying individuals at risk; to examine carefully to determine suitability. Cross-section survey - A survey that collects data using random sampling; a survey that gives all individuals or household in the study area an equal chance of being chosen for the survey. Simple random sample - The results of a method of sampling that gives everyone an equal chance of being selected; the simplest form of probability sampling; a sample in which an individual’s selection is independent of the selection of any other individual. Standard deviation - A statistical measure of dispersion away from the mean; the square root of the variance. Stratified sample - A method of sampling that ensures proportional representation from all subgroups or strata. Stratified survey - A survey that chooses participants randomly after they have been divided into the applicable strata or subgroups. Stunting - A slowing of skeletal growth that results in reduced stature or length; a condition that usually results from extended periods of inadequate food intake and infection, especially during the years of greatest growth for children. Survey - A method of gathering information about a large number of people by talking to a few of them; a way to collect information on people’s needs, behaviour, attitudes, environment and opinions, as well as on such personal characteristics as age, income and occupation. Systematic sample - A modification of a simple random sample that consists of picking individuals at regular intervals from a random list. Underweight - A condition measured by weight-for-age; a condition that can also act as a composite measure of stunting and wasting. Variable - A quantity that may vary from object to object; a characteristic of a unit. Wasting - A condition measured by weight-for-height; a condition that results from the loss of both body tissue and fat in a body; a condition that usually reflects severely inadequate food intake and infection happening at present. Weight-for-age - An index of short and long term malnutrition referred to as under-nutrition; a valuable index for use with very young children or when length measurements are difficult to do accurately. Weight-for-height - An index of current nutritional status also referred to as wasting. Z-score - A statistical measure of the distance, in units of standard deviations, of a value from the mean; the standardized value for an item based on the mean and standard deviation of a data set; a standardized value computed by subtracting the mean from the data value and then dividing the results by the standard deviation.

References

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